{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'BUFSIZE',\n",
       " 'CLIP',\n",
       " 'DataSource',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'WRAP',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_builtins',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_typing',\n",
       " '_using_numpy2_behavior',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'expm1x',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "\n",
    "# 设置 token 并初始化\n",
    "ts.set_token('b8feb479c2c56b41ec975661c9f71b88f025e962e7f4e96e2339a6d9')\n",
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000025.SZ   20241225  17.24  17.28  16.75  16.92      17.23   -0.31   \n",
      "1     000025.SZ   20241224  17.15  17.29  16.70  17.23      17.06    0.17   \n",
      "2     000025.SZ   20241223  17.67  17.68  16.99  17.06      17.67   -0.61   \n",
      "3     000025.SZ   20241220  17.86  17.96  17.60  17.67      17.87   -0.20   \n",
      "4     000025.SZ   20241219  17.92  18.05  17.61  17.87      18.05   -0.18   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3600  000025.SZ   20100108  10.96  11.14  10.92  11.14      11.05    0.09   \n",
      "3601  000025.SZ   20100107  11.23  11.39  10.83  11.05      11.32   -0.27   \n",
      "3602  000025.SZ   20100106  11.20  11.50  11.10  11.32      11.21    0.11   \n",
      "3603  000025.SZ   20100105  11.19  11.29  10.79  11.21      11.19    0.02   \n",
      "3604  000025.SZ   20100104  11.39  11.43  11.11  11.19      11.28   -0.09   \n",
      "\n",
      "      pct_chg       vol       amount  \n",
      "0     -1.7992  41628.50   70381.6010  \n",
      "1      0.9965  47981.90   82120.2780  \n",
      "2     -3.4522  60407.15  104006.4480  \n",
      "3     -1.1192  47712.60   84802.0650  \n",
      "4     -0.9972  46004.26   81854.2920  \n",
      "...       ...       ...          ...  \n",
      "3600   0.8100  10635.58   11760.6045  \n",
      "3601  -2.3900  16368.40   18189.5151  \n",
      "3602   0.9800  17206.40   19523.0658  \n",
      "3603   0.1800  15657.67   17349.3633  \n",
      "3604  -0.8000  13618.11   15250.5334  \n",
      "\n",
      "[3605 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('b8feb479c2c56b41ec975661c9f71b88f025e962e7f4e96e2339a6d9')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000025.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000025.SZ   20241225  17.24  17.28  16.75  16.92      17.23   -0.31   \n",
      "1     000025.SZ   20241224  17.15  17.29  16.70  17.23      17.06    0.17   \n",
      "2     000025.SZ   20241223  17.67  17.68  16.99  17.06      17.67   -0.61   \n",
      "3     000025.SZ   20241220  17.86  17.96  17.60  17.67      17.87   -0.20   \n",
      "4     000025.SZ   20241219  17.92  18.05  17.61  17.87      18.05   -0.18   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3600  000025.SZ   20100108  10.96  11.14  10.92  11.14      11.05    0.09   \n",
      "3601  000025.SZ   20100107  11.23  11.39  10.83  11.05      11.32   -0.27   \n",
      "3602  000025.SZ   20100106  11.20  11.50  11.10  11.32      11.21    0.11   \n",
      "3603  000025.SZ   20100105  11.19  11.29  10.79  11.21      11.19    0.02   \n",
      "3604  000025.SZ   20100104  11.39  11.43  11.11  11.19      11.28   -0.09   \n",
      "\n",
      "      pct_chg       vol       amount  \n",
      "0     -1.7992  41628.50   70381.6010  \n",
      "1      0.9965  47981.90   82120.2780  \n",
      "2     -3.4522  60407.15  104006.4480  \n",
      "3     -1.1192  47712.60   84802.0650  \n",
      "4     -0.9972  46004.26   81854.2920  \n",
      "...       ...       ...          ...  \n",
      "3600   0.8100  10635.58   11760.6045  \n",
      "3601  -2.3900  16368.40   18189.5151  \n",
      "3602   0.9800  17206.40   19523.0658  \n",
      "3603   0.1800  15657.67   17349.3633  \n",
      "3604  -0.8000  13618.11   15250.5334  \n",
      "\n",
      "[3605 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"000025.csv\")\n",
    "stockname='\t特 力Ａ'\n",
    "stockcode='000025'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3595</th>\n",
       "      <th>3596</th>\n",
       "      <th>3597</th>\n",
       "      <th>3598</th>\n",
       "      <th>3599</th>\n",
       "      <th>3600</th>\n",
       "      <th>3601</th>\n",
       "      <th>3602</th>\n",
       "      <th>3603</th>\n",
       "      <th>3604</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "      <td>000025.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>17.24</td>\n",
       "      <td>17.15</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.86</td>\n",
       "      <td>17.92</td>\n",
       "      <td>17.72</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.33</td>\n",
       "      <td>18.65</td>\n",
       "      <td>18.48</td>\n",
       "      <td>...</td>\n",
       "      <td>12.15</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.68</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.16</td>\n",
       "      <td>10.96</td>\n",
       "      <td>11.23</td>\n",
       "      <td>11.2</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>17.28</td>\n",
       "      <td>17.29</td>\n",
       "      <td>17.68</td>\n",
       "      <td>17.96</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.13</td>\n",
       "      <td>18.27</td>\n",
       "      <td>18.55</td>\n",
       "      <td>18.65</td>\n",
       "      <td>18.73</td>\n",
       "      <td>...</td>\n",
       "      <td>12.47</td>\n",
       "      <td>12.22</td>\n",
       "      <td>12.13</td>\n",
       "      <td>11.92</td>\n",
       "      <td>11.51</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.5</td>\n",
       "      <td>11.29</td>\n",
       "      <td>11.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>16.75</td>\n",
       "      <td>16.7</td>\n",
       "      <td>16.99</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.61</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.66</td>\n",
       "      <td>18.11</td>\n",
       "      <td>18.25</td>\n",
       "      <td>18.4</td>\n",
       "      <td>...</td>\n",
       "      <td>11.85</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.6</td>\n",
       "      <td>11.32</td>\n",
       "      <td>11.16</td>\n",
       "      <td>10.92</td>\n",
       "      <td>10.83</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.79</td>\n",
       "      <td>11.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>16.92</td>\n",
       "      <td>17.23</td>\n",
       "      <td>17.06</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.87</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.73</td>\n",
       "      <td>18.27</td>\n",
       "      <td>18.25</td>\n",
       "      <td>18.64</td>\n",
       "      <td>...</td>\n",
       "      <td>12.42</td>\n",
       "      <td>12.19</td>\n",
       "      <td>11.81</td>\n",
       "      <td>11.88</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.32</td>\n",
       "      <td>11.21</td>\n",
       "      <td>11.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>17.23</td>\n",
       "      <td>17.06</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.87</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.73</td>\n",
       "      <td>18.27</td>\n",
       "      <td>18.25</td>\n",
       "      <td>18.64</td>\n",
       "      <td>18.54</td>\n",
       "      <td>...</td>\n",
       "      <td>12.19</td>\n",
       "      <td>11.81</td>\n",
       "      <td>11.88</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.32</td>\n",
       "      <td>11.21</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-1.7992</td>\n",
       "      <td>0.9965</td>\n",
       "      <td>-3.4522</td>\n",
       "      <td>-1.1192</td>\n",
       "      <td>-0.9972</td>\n",
       "      <td>1.8049</td>\n",
       "      <td>-2.9557</td>\n",
       "      <td>0.1096</td>\n",
       "      <td>-2.0923</td>\n",
       "      <td>0.5394</td>\n",
       "      <td>...</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.22</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>3.94</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.81</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>41628.5</td>\n",
       "      <td>47981.9</td>\n",
       "      <td>60407.15</td>\n",
       "      <td>47712.6</td>\n",
       "      <td>46004.26</td>\n",
       "      <td>54011.65</td>\n",
       "      <td>68017.96</td>\n",
       "      <td>67148.35</td>\n",
       "      <td>76997.07</td>\n",
       "      <td>80354.4</td>\n",
       "      <td>...</td>\n",
       "      <td>37404.27</td>\n",
       "      <td>26346.53</td>\n",
       "      <td>25215.33</td>\n",
       "      <td>31714.08</td>\n",
       "      <td>18661.24</td>\n",
       "      <td>10635.58</td>\n",
       "      <td>16368.4</td>\n",
       "      <td>17206.4</td>\n",
       "      <td>15657.67</td>\n",
       "      <td>13618.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>70381.601</td>\n",
       "      <td>82120.278</td>\n",
       "      <td>104006.448</td>\n",
       "      <td>84802.065</td>\n",
       "      <td>81854.292</td>\n",
       "      <td>96742.621</td>\n",
       "      <td>121570.341</td>\n",
       "      <td>123166.689</td>\n",
       "      <td>141614.997</td>\n",
       "      <td>149467.073</td>\n",
       "      <td>...</td>\n",
       "      <td>45642.3721</td>\n",
       "      <td>31785.0163</td>\n",
       "      <td>29944.0898</td>\n",
       "      <td>37327.7774</td>\n",
       "      <td>21202.5044</td>\n",
       "      <td>11760.6045</td>\n",
       "      <td>18189.5151</td>\n",
       "      <td>19523.0658</td>\n",
       "      <td>17349.3633</td>\n",
       "      <td>15250.5334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3605 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0          1           2          3          4          5     \\\n",
       "ts_code     000025.SZ  000025.SZ   000025.SZ  000025.SZ  000025.SZ  000025.SZ   \n",
       "trade_date   20241225   20241224    20241223   20241220   20241219   20241218   \n",
       "open            17.24      17.15       17.67      17.86      17.92      17.72   \n",
       "high            17.28      17.29       17.68      17.96      18.05      18.13   \n",
       "low             16.75       16.7       16.99       17.6      17.61       17.6   \n",
       "close           16.92      17.23       17.06      17.67      17.87      18.05   \n",
       "pre_close       17.23      17.06       17.67      17.87      18.05      17.73   \n",
       "change          -0.31       0.17       -0.61       -0.2      -0.18       0.32   \n",
       "pct_chg       -1.7992     0.9965     -3.4522    -1.1192    -0.9972     1.8049   \n",
       "vol           41628.5    47981.9    60407.15    47712.6   46004.26   54011.65   \n",
       "amount      70381.601  82120.278  104006.448  84802.065  81854.292  96742.621   \n",
       "\n",
       "                  6           7           8           9     ...        3595  \\\n",
       "ts_code      000025.SZ   000025.SZ   000025.SZ   000025.SZ  ...   000025.SZ   \n",
       "trade_date    20241217    20241216    20241213    20241212  ...    20100115   \n",
       "open              18.2       18.33       18.65       18.48  ...       12.15   \n",
       "high             18.27       18.55       18.65       18.73  ...       12.47   \n",
       "low              17.66       18.11       18.25        18.4  ...       11.85   \n",
       "close            17.73       18.27       18.25       18.64  ...       12.42   \n",
       "pre_close        18.27       18.25       18.64       18.54  ...       12.19   \n",
       "change           -0.54        0.02       -0.39         0.1  ...        0.23   \n",
       "pct_chg        -2.9557      0.1096     -2.0923      0.5394  ...        1.89   \n",
       "vol           68017.96    67148.35    76997.07     80354.4  ...    37404.27   \n",
       "amount      121570.341  123166.689  141614.997  149467.073  ...  45642.3721   \n",
       "\n",
       "                  3596        3597        3598        3599        3600  \\\n",
       "ts_code      000025.SZ   000025.SZ   000025.SZ   000025.SZ   000025.SZ   \n",
       "trade_date    20100114    20100113    20100112    20100111    20100108   \n",
       "open              11.8       11.68       11.43       11.16       10.96   \n",
       "high             12.22       12.13       11.92       11.51       11.14   \n",
       "low               11.8        11.6       11.32       11.16       10.92   \n",
       "close            12.19       11.81       11.88       11.43       11.14   \n",
       "pre_close        11.81       11.88       11.43       11.14       11.05   \n",
       "change            0.38       -0.07        0.45        0.29        0.09   \n",
       "pct_chg           3.22       -0.59        3.94         2.6        0.81   \n",
       "vol           26346.53    25215.33    31714.08    18661.24    10635.58   \n",
       "amount      31785.0163  29944.0898  37327.7774  21202.5044  11760.6045   \n",
       "\n",
       "                  3601        3602        3603        3604  \n",
       "ts_code      000025.SZ   000025.SZ   000025.SZ   000025.SZ  \n",
       "trade_date    20100107    20100106    20100105    20100104  \n",
       "open             11.23        11.2       11.19       11.39  \n",
       "high             11.39        11.5       11.29       11.43  \n",
       "low              10.83        11.1       10.79       11.11  \n",
       "close            11.05       11.32       11.21       11.19  \n",
       "pre_close        11.32       11.21       11.19       11.28  \n",
       "change           -0.27        0.11        0.02       -0.09  \n",
       "pct_chg          -2.39        0.98        0.18        -0.8  \n",
       "vol            16368.4     17206.4    15657.67    13618.11  \n",
       "amount      18189.5151  19523.0658  17349.3633  15250.5334  \n",
       "\n",
       "[11 rows x 3605 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQoAAAFdCAYAAABLmVAEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACS80lEQVR4nOzddXxT9/oH8E+87i2FllLB3V3Ghg3GHXPXO72783F/Y2PbnQF37u7GlBkbMgbDhhUvRQqlUFrqbZpK/Pz+SHNyYhVIm6b9vF+vvZacc5Kc0hCST57v88gEQRBAREREREREREREnZrc3ydARERERERERERE/segkIiIiIiIiIiIiBgUEhEREREREREREYNCIiIiIiIiIiIiAoNCIiIiIiIiIiIiAoNCIiIiIiIiIiIiAoNCIiIiIiIiIiIiAqD09wk0xWq1orCwEOHh4ZDJZP4+HSIiIiIiIiIiooAiCAJ0Oh26desGudx73WC7DwoLCwvRvXt3f58GERERERERERFRQMvPz0dycrLX/e0+KAwPDwdg+0EiIiL8fDZERERERERERESBpbq6Gt27dxdzNm/afVBoX24cERHBoJCIiIiIiIiIiOgMNdXWj8NMiIiIiIiIiIiIiEEhERERERERERERMSgkIiIiIiIiIiIiBECPQiIiIiIiIiIi6rgsFgtMJpO/TyOgqVQqKBSKs74fBoVERERERERERNTmBEFAUVERqqqq/H0qHUJUVBQSExObHFjSGAaFRERERERERETU5uwhYUJCAkJCQs4q4OrMBEFAXV0dSkpKAABdu3Y94/tiUEhERERERERERG3KYrGIIWFsbKy/TyfgBQcHAwBKSkqQkJBwxsuQOcyEiIiIiIiIiIjalL0nYUhIiJ/PpOOw/1meTb9HBoVEREREREREROQXXG7sO774s2RQSERERERERERERAwKiYiIiIjIO73Jgge+2YPf9p3296kQERG1e3l5eQFdJcmgkIiIiIiIvPr07zws212Au77a5e9TISIiolbGoJCIiIiIiLwqrjb4+xSIiIiojbQ4KCwvL0daWhry8vLEbVlZWRg1ahSio6Mxf/58CILQrH1EREREREREREQAIAgC6ozmNv+vpVnV7t27MW7cOISFhWHChAk4cOBAk7fJysrCxIkTERkZidmzZ+PUqVPivtWrV6Nfv34ICQnBhAkTcOzYMXHfypUrMWjQIERFReGWW26BwdC6X+ApW3JwWVkZ5s6d6xQSGgwGzJ07FzNnzsTXX3+Ne+65B5988gluuummRvcRERERERERERHZ1Zss6P/4qjZ/3OynZiJE3byIrLq6GrNmzcJ9992H77//Hi+99BKuueYa7Nmzx+ttampqMGPGDNx+++348ssvsWjRIlx44YXYsWMH5HI5rr/+ejz00EO48sor8fTTT2PhwoVYunQpjh07hgsvvBBvv/02pkyZgksvvRTPP/88Fi5c6KOf3F2LKgqvvPJKXHnllU7bVqxYAa1Wi5deegkZGRlYtGgRPvzwwyb3ERERERFR+yeAK4KIiIjsli9fjpiYGCxYsABJSUlYuHAhFixY0Ohtfv31V4SHh+OJJ55Ajx498NprryEnJwfbt28HAAQHB8NgMCAyMhLvvPMOvvjiCwDA0qVLMWzYMNx8883IyMjAHXfcgV9++aVVf74WVRS+9957SE9Px3333Sdu27t3L8aOHYuQkBAAwODBg5Gdnd3kPm8MBoNTGWV1dXVLTpGIiIiIiIiIiAJQsEqB7Kdm+uVxm+vUqVNITU0Vr0dHR+OKK65o9Db5+flIS0sTr2s0GnTr1g35+fkYO3Ysli5disceewyLFy/GkCFD8Morr2DUqFEoKCjArl27EBUVBQAwm80ICwtr0c/WUi0KCtPT0922VVdXO/2wMpkMCoUClZWVje6Ljo72+BiLFy/Gk08+2ZLTIiIiIiKiVsIW40RE1FZkMlmzlwD7S/fu3XH8+HHxek1NDcaOHYs1a9YgMTHR421SUlKcbqPX61FYWIiUlBTU1taitrYWf/zxB4xGIx5//HHcfPPN2L9/P5KTk/GPf/wDL7zwAgDAYrGgrq6uVX++s556rFQqodFonLYFBQWhrq6u0X3eLFiwAFqtVvwvPz//bE+RiIiIiIiIiIjorM2ZMweVlZVYtGgRTp06hWeeeQYWiwVdunTxepsLLrgAOp0OTz75JE6cOIF7770XvXr1wqhRo2C1WjFnzhx88cUXKCsrg1wuh9VqBQBcddVV2LhxI3JycgAAr776aqvP/TjroDAmJgalpaVO23Q6HdRqdaP7vNFoNIiIiHD6j4iIiIiIiIiIyN8iIiKwcuVK/Prrr+jXrx+2bt2KH3/8ETKZzOttwsLCsGrVKqxevRqDBg3CyZMn8fPPP0MulyM8PBxffPEFnn32WWRkZODXX3/F22+/DcC2svfTTz/FAw88gAEDBiArKwtLly5t1Z/vrOs5R40ahQ8++EC8npeXB4PBgJiYmEb3ERERERFRYBEEodEPQkRERJ3BsGHDsGXLFo/7UlNTIXjo2zFo0CBs3rzZ420uvfRSXHrppR73zZo1C7NmzTrzk22hs64onDx5MrRaLT777DMAwJIlSzBt2jQoFIpG9xERERERUfsn/bBjtrJhIRERUUd21hWFSqUS7733Hq6++mrMnz8fFosF69evb3IfERERERG1f9Jo0GwR0ILBkERERBRgzigodC2hnDdvHnJycpCZmYnx48cjPj6+WfuIiIiIiKh9k771N1mtCAaTQiIioo7KZzOnk5KSkJSU1OJ9REREREQUGOqNFoSqlVDI2aeQiIioIzrrHoVERERERNRxma1W8fKYRX9ixstsJURERL5jlfw7Q2fHF3+WPqsoJCIiIiKijqfWYHG6fqy0FlarADmrComI6Cyo1WrI5XIUFhYiPj4earUaMhn/bTkTgiDAaDSitLQUcrkcarX6jO+LQSEREREREXlVZ7S4bbMIAuTghzkiIjpzcrkcaWlpOH36NAoLC/19Oh1CSEgIUlJSIJef+QJiBoVERERERORVndHsts1i5fRjIiI6e2q1GikpKTCbzbBY3L+YouZTKBRQKpVnXZXJoJCIiIiIiLyq9VRRaBU8HElERNRyMpkMKpUKKpXK36dC4DATIiIiIiJqRL2HikIzg0IiIqIOiUEhERERERF5pa03uW2zMigkIiLqkBgUEhERERGRR6sOFKG42uC23SIwKCQiIuqIGBQSEREREZFHt3++0+N29igkIiLqmBgUEhERERGRm4Kqeq/7GBQSERF1TAwKiYiIiIjIzYPf7vG6j0EhERFRx8SgkIiIiIiI3Bwu0nndx6CQiIioY2JQSEREREREbhRy7x8VzAwKiYiIOiQGhURERERE5MRgtqCsxn3asZ2VU4+JiIg6JAaFREREREQksloF9Fm4stFjzBYGhURERB0Rg0IiIiIiIhLpDOYmj2FFIRERUcfEoJCIiIiIiEQmi7XJYzjMhIiIqGNiUEhERERERCK9yeJ0/a+HznE7hsNMiIiIOiYGhUREREREJJIGhSFqBVLjQt2O4dJjIiKijolBIRERERERifQmx9JjpVzm8RgOMyEiIuqYGBQSEREREZFIWlGoVnr+uMCKQiIioo6JQSEREREREYmkFYUqheePC+xRSERE1DExKCQiIiIiItGRYp14WanwvPTY6iEoLNUZWu2ciIiIqG0wKCQiIiIiItGKrNPiZZW8eRWFn2/Jw6hn1+D1P3Na9dyIiIiodTEoJCIiIiIiGMwWFFfroZAMMPFWUWhxCQof+/kAAODFP4603gkSERFRq1P6+wSIiIiIiMj/Zr+6EcdKa50mHSu9VBRymAkREVHHxIpCIiIiIiLCsdJaAM7LilVeph67Lj2Wey48JCIiogDDoJCIiIiIiDxSNyw9vmNKhtN212EmSi/TkYmIiCiw8F90IiIiIiLyaHhKNADgwRm9nba7VhRKlyu/soZ9ComIiAIVg0IiIiIiok5O8NBz8IZxPXDfNFtAqJA5ry12rShUOAWFOcgq0LbCWRIREVFrY1BIRERERNTJGS1Wt20PzOiDYLUCACB3aULoWlGocll6XFVn8vEZEhERUVtgUEhERERE1Mnpje5BoUrhfUKJRfBeUQgAAjgVmYiIKBAxKCQiIiIi6uTqTRa3ba7hn5TFpQJRxbHHREREHQKDQiIiIiKiTq7OaHbbppJ7/6hgcSkY5NRjIiKijoH/ohMRERERdXI6vXNQKJO59yWUch1monRdesyVx0RERAGJQSERERERUSenrXcePtJYNSHgPswEXHlMRETUITAoJCIiIiLq5FyDwsb6EwKA1aVk0PVoFhQSEREFJgaFRERERESdnGtQqGxk4jEAmCVNCour9ThWWuu0X+DaYyIiooDEoJCIiIiIqJNz7VHo2nPQlUUSBD7z28FWOSciIiJqewwKiYiIiIg6ObPF6nS9qSnGFqvj+CJtvdt+1hMSEREFJgaFRERERESdnKmJKcaupLlicnSI237XqchEREQUGBgUEhERERF1ctIKQaDpHoXS44NU7h8p3KYiExERUUBgUEhERERE1IllFWix80Sl0zalvKmlx40fa2FQSEREFJCU/j4BIiIiIiLyD73Jggte3+S2vamlx1bJMBOVh36GrCgkIiIKTKwoJCIiIiLqpLT1Jo/bFU0EhWbJ0mNPy5RdlzITERFRYGBQSERERETUSdUZLR63e6oSlJIuPfY0uMRsYUUhERFRIPJZUPj5558jJSUFYWFhmDZtGvLy8gAAWVlZGDVqFKKjozF//nwIAt80EBERERG1Bzr9mVUUSisGLR7e31v5np+IiCgg+SQoPHbsGB599FH89NNPyM7ORo8ePXDjjTfCYDBg7ty5GDFiBDIzM5GdnY1PPvnEFw9JRERERERnSac3e9yuanLqseOyx4pC9igkIiIKSD4JCnfv3o2xY8di+PDhSElJwU033YQjR45gxYoV0Gq1eOmll5CRkYFFixbhww8/9MVDEhERERHRWWqtikJOPSYiIgpMPpl63L9/f6xduxa7d+9Geno63nzzTUyfPh179+7F2LFjERISAgAYPHgwsrOzG70vg8EAg8EgXq+urvbFKRIRERERkYt6k+cehUp5Ez0KJTmgp1CQPQqJiIgCk08qCvv3749LL70Uw4cPR1RUFLZt24YXXngB1dXVSEtLE4+TyWRQKBSorKz0el+LFy9GZGSk+F/37t19cYpEREREROTC5CXQ8zTJWMqpotBDUMiKQiIiosDkk6Bw69at+PXXX7Ft2zbodDpcddVVmD17NpRKJTQajdOxQUFBqKur83pfCxYsgFarFf/Lz8/3xSkSEREREZGL6nrPS4+VTS49FiSX3fezRyEREVFg8klQ+M033+DKK6/E6NGjERYWhmeeeQa5ubmIiYlBaWmp07E6nQ5qtdrrfWk0GkRERDj9R0REREREvpVXVotnfjvocZ+npcdTeseLl6XhoLS60I5Tj4mIiAKTT4JCs9mM4uJi8bpOp0NtbS2USiW2bt0qbs/Ly4PBYEBMTIwvHpaIiIiIiM7Ql9tOeN2n8LD0+IMbRuLfU3sCcB1m4n579igkIiIKTD4JCidMmIBly5bh5ZdfxldffYV58+ahS5cuuOeee6DVavHZZ58BAJYsWYJp06ZBoVD44mGJiIiIiOgMBau8vydXeVh6rFLIkR4fCsA5HLR67FHoYT0yERERtXs+mXp8xRVX4PDhw3jllVdw+vRpDBw4EMuWLYNKpcJ7772Hq6++GvPnz4fFYsH69et98ZBERERERHQWgtXePwoovEw9VjQEiNIg0OwhFGSPQiIiosDkk6BQJpPhiSeewBNPPOG2b968ecjJyUFmZibGjx+P+Ph4D/dARERERERtKVjlfXGRysvUY0dQ2PgwE049JiIiCkw+CQqbkpSUhKSkpLZ4KCIiIiIiaoZgtfelxwovU48VMveg0NPgElYUEhERBSaf9CgkIiIiIiL/KtUZsHjFQeSV1TbreG/LiwFbP0LPt/FUUeipRyGDQiIiokDEoJCIiIiIqAP4vx/24d31ubj47b+bdbzrwJFbJqaJl71WFDIoJCIi6tAYFBIRERERdQA7T1QCACpqjV6PqTWYkV1YDcB5efCkXnGYNTBRvK700qNQbg8KhcaDQi49JiIiCkxt0qOQiIiIiIhal0bZdA3A1R9sw978Knx1yxingE9vsjgtN1Z6qSi0bzdbJEGhhx6FrtWKREREFBhYUUhERERE1AFoGpliDAAGswV786sAAOuPlDqFfXqT1Wm5sdJL/0L7MBMrKwqJiIg6JAaFREREREQdgEbpfYoxACxZcUi8nBoX6hTwGcwWp+XG3ioKPfUorDda3I5jj0IiIqLAxKCQiIiIiKgDaGzpsSAI+HhznnjdYhWcqv4enNHHKRxUtmDqsd7EoJCIiKijYFBIRERERNQByGWeqwABwGB27hlosQpiH8Hp/btg5oBEKORN9yhUeBhmUseKQiIiog6DQSERERERkR8IgoCPNh3HxpxSn9yf1cNQETvXMM8sqSjsEqEB4BwOKpoKChv6G5otVuj0Jrfj2KOQiIgoMHHqMRERERGRH2zMKcNTy7MBAHlL5pz1/TVWxffNjnyXY63i8fbBJdJwUKVouqJQEATMfGUDallRSERE1GGwopCIiIiIyMcEQYDJYm30mP0FWh8/pvd9/1t5yOm6tKLQHv45VxQ23aOw3mTBsdJaj8exopCIiCgwMSgkIiIiIvKxf325C2MW/QltvfOy3HWHSvDlthMAgFKdwaePaWksKXSx/nApCirrATgCQmlFodcehTJHUGg0ew9C7f0PiYiIKLBw6TERERERkY+tyCoCAKzMOo0rRqWI22/6ZAcAYEhyFAqq6n3yWL/sLcQ9S3c7bbNaBci9hH0AsO14hXj5VJU9MJTUEHi5qT1MNFsFtwEpUlx6TEREFJgYFBIREREReWG1CtiQU4qBSZGIC9O0+PZBKoXH7QVV9ThSrDurc1v8+0GsPVQCvdlDj0BBgFyS9g1PicKuk1Ue7+fPg8UAAIWkL6G3iNEeJlqsAgwmR1CokMucwkEGhURERIGJQSERERERkRc/7SnAA9/uRUyoGrsem96s20hDMo3Sc6cfk8Xqtiy5pd7dkNvoOUgzyphQNQAgTKNEjcHsdizgfbmxlFIhrSh0BJTxYRoUVevF6+xRSEREFJjYo5CIiIiIyItVB2xLiCtqjc2+TZ3REcRplI60ziwZbmKyWGG2OMI0q4+DNWlQt+pAEXY3VBMGqdzf/r9w2RAAzj0KZTLPoaE9TDRbrCitcfRYVLsEoqwoJCIiCkysKCQiIiIi8qKJwcUe1RkdlXbSPoEmSTBoMgswSwZ+mK0C1M2o6LNrKli0h5IHCrW4/fOd4nZpcGk3Ji0WgGNQSWPsYaJVAP75Saa4XaVwvq00BCUiIqLAwYpCIiIiIiIvhBZMEraTLu2VTv81WpwvS8O0llbg1Znc+xJK2SsKDxc590EMVrsHhfblxNJQ02uPQoXj40O95BxUClYUEhERdQQMComIiIiIvLCcQVBYKwkKpWGgSRoUmq1Oy4NN1paVLpbpDI3uf37lYfFxpDwtPW5Ob8KmjnULCs/gz42IiIj8j0EhERERERGArbnluPWzTBRU1YvbzqQyrrLOMaREentpUFjvUhFoaeFS3W8y85u13+ASFMo81AoqFe4fCbytQlZ4DQpluGNKhnidFYVERESBiT0KiYiIiIgAXPneVgBAvdGCL24ZAwCwnkFlXKVk8Im0alBa3VfrMnm4pRWFzS0ClE4mBpzDSruWVBS6Vg7aXT6yO64Y1R0DukXg7qW7nfovEhERUeBgUEhEREREJHGiola8fCaVcdIJyd4qCl2DwpY+juuS4uYep9Ob3Y7xFP55qyh0zRR7dwnD0xcOxKjUGMhkMqTGhgJoeYUkERERtQ9cekxEREREJCGXpGRnsoK2ss4RFDr3JXTcmbbe5HSblk4Jdg0Aw4M8f//vuvTY9XEBz9WJci9JoUwmc5pwHBWixpj0WHEQirzh04WZS4+JiIgCEoNCIiIiIiIJaURmbUbg9ePuU/hx9ynxuqeKQoPZgove2ixuX3e41Ok+Whqs2QPAa8ak4JObRmH3Y9Px+lXDnI6xWgWnScsAkBQV7HZfMkkoeNXo7kiPD8XMAYleH1vap9B12bKyISlkj0IiIqLAxKXHREREREQS0uDM2/Te42W1uPWzTMzo3wVv/XUMAJASE4LvdxZg6faT4nH2ADAzr9Kpus+9orBlPf3s95UaG4pz+iQAAOYO6Ya7l+52Osb19B+d0w8/7SnAsl0FHu938cWDIQiC05+BK1sYaHt81+Em9uucekxERBSYGBQSERERUaenl0whbk5F4dQX/gIAHC2pEbfd8cUulOoMTsfZK+s0ysYX8rSkovC0th4/7rYFfRqV9/vVmyxulX0DukVgcu94r0EhgEZDQgBQSpYeu/Y3tFcYskchERFRYOLSYyIiIiLq9G76eId4WS6pknPt8Qd4r/5zDQkBRwDY1PCRlvQofPTHLPGyawA5oFuEeNlgtjoFhRFBSsSGaZr9ON5Ilxt7qyhkj0IiIqLAxKCQiIiIiDotQRBQVWfEltxycZs9CKusNeJQkc7tNvWS6sOm2ENFT4Gj03HW5i89zi11VDFqlAqnfS9cNkS8rDdZnIapuIZ6Z8reh9B22aVHYUO1IXsUEhERBSYGhURERETUaS38KQtDn/rDaduhIh1KdQZMfn6dx9s0FfoBQEK4rXLPLA4z8XybULXC6bjGrMw6jcnPrUNeeZ24zbWisF/XCMSFqQEAdUaLUyVjU0uKm8tpmInL0mOFzF5R2LKei0RERNQ+MCgkIiIiok7ry20nPW6/5oOt0OnNTtvs04z1zagoTIkJAWCrrKszmt2mD9vFNIR6zVl6vPCnLJysqHPapvbQ+zAiSAUAqNab8N1OxzRmaUz4/vUjm3w8b6Q9Cl0rCu0holWwVWsSERFRYGFQSERERESdUnG13un6zoXTMG9oNwDAkeIat+Ovfn8rAM/VgRcM7orYULV4Pb6honDniUoM/u9q3COZRmw3KjUaoWrbbMHmVOCV1RjdtnWLCnbbFhFsCwrXHSpx2i4tKJzWLwGPzO6LpbeObfJxXTXWo1C6LJnLj4mIiAIPg0IiIiIi6nQMZgvGLf5TvP7B9SMRG6bBK1cO83qbQ0U6vLj6MH7eU+i0/eJhSXjj6uFYff9kcVuXiCAAwPojpV6XFT98fj9xavCZDv/olRDmti2yISjcKum7CDgvPZbJZLhtcgbGZcS2+DEb61GokFQbcqAJERFR4FH6+wSIiIiIiNra4SIdpDnWtP5dmnW719ceFS8nRQXjsQv6Y3xPW9gWG6bBi5cNQZBKgRMVtU3eV3SIyjEluImlxyaXpcsRQUq8f/1Itx6BgKOicO8prdN233QodO1R6FpR6LjOikIiIqLAw6CQiIiIiDod6UAQV29dMxzL9xWizmjBX4dLvR4XpJJj1sBEp22XjEgGAHyy+XiT5xAdooZKYQ8KG196rK03iZe3LDgXXSPdlxzbRQR5fovvo1km4jkDztWFgHOI2Jy+i0RERNS+MCgkIiIiok7HJOkzODotxmnf7EFdMXtQVwiCgGW7CvDTngJszClzu49jpd6rBl0HoXgSESypKGyi+q66ISgM1ygbDQkBiMuZXcl8VFPo2pdQSlpRyMnHREREgYc9ComIiIio05Eu5X332hEej5HJZLhkRDI+u3k0QtWKFt3/jAGJTR6jkMskPQqbV1FoX1bcGG/Thq0+mkIsXe686kCR0z6ZTNbs8JOIiIjaHwaFRERERNTpmBpCrFkDEhEtmVbsiUwmQ9aTM/HyFUPQIzZE3P7G1d4Hn/RJDMdfD52DhXP64ZLhyeL2i4cnOR3X3B6F9UYLACBU03Rg6S2f05ssTd62ORSSNcyntXq3/faqQte+ikRERNT+cekxEREREXU69qXHrsM4vJHJZLhoWDKm90/Ea3/mYHxGLM7pk9DobVLjQnHLpHQIgoCYUBWSo0NgNFsBFIjH2Hv8NVV9Z2g4X42y6aDQ29Ljeh8FhdIhJef0iff4+AazlT0KiYiIAhCDQiIiIiLqdOxLfdVeQjVvwjRKPDK7X4tuI5PJ8Oic/gBsVX2lNQac19cWMiqbuUzXYLaFfBpl0+d7+5R0fORhmIrJR8FdncnRf/GFy4a47beHr+xRSEREFHi49JiIiIiIOh17aNbcikJfCVIp8MjsfhiTHuv0+E1NPRYrClVNv33vEhGEP+6fjIggJe6b1ussz9hdndFRmRgXpnHbb6+S9FUwSURERG2HFYVERERE1OnY++cpW1hR6Gv2ikJLUxWFpuYvPQaAXl3CsefxGZDLZcgq0GLNwRLMHNDl7E62gd7Y+BJmlaJ5fReJiIio/WFQSERERESdjj3EaunSY1+zB5VNVd+1ZOmxnbwhhHzpiqFYfaAYM3wUFJqaCDXtVZJGDjMhIiIKOAwKiYiIiKjTMTX0z7NX9PmLo6KwmUuPWxAU2kUEqXDpiOSmD2ympqof7cNUmlpOTURERO0PexQSERERUadjMtvCLtUZBG++ZK++a7qisGVLj1uTqYkAUNXMSc5ERETU/jAoJCIiIqJOxz6RV+X3ikLb2/GmqvT0poalx80YZtLamuo96Ag/WVFIREQUaPz/ToOIiIiIqI05ph63j2EmpkaWHguCgNfXHgVwZkuPfc3cxDJppbj0mBWFREREgaZV3mk8/PDDmDt3rng9KysLo0aNQnR0NObPnw9B4JsGIiIiIvIfe7Wbyt9BYcPjWxoJ1Y6X1YqXx6bHtvo5NaWpZdL2Ks2mAkUiIiJqf3z+zigrKwtvvfUWXnnlFQCAwWDA3LlzMWLECGRmZiI7OxuffPKJrx+WiIiIiKjZzGJQ6O+lx/ZQzXv4VllnBAB0jwnGef18M7m4NTW37yIRERG1Pz4NCgVBwO2334777rsPGRkZAIAVK1ZAq9XipZdeQkZGBhYtWoQPP/zQlw9LRERERNQi4tJjf/coVDRdfVdrsPUnDNOo2uSczpY49ZgVhURERAHHp0Hh+++/jz179iAtLQ3Lly+HyWTC3r17MXbsWISEhAAABg8ejOzsbK/3YTAYUF1d7fQfEREREZEviUuP/T312F5R2Ej1Xa3BDAAIVft/4jEALLpoEADgkdl9Pe4X+y6yopCIiCjg+OydUU1NDRYuXIhevXrh1KlTeOmllzB58mRUV1cjLS1NPE4mk0GhUKCystLj/SxevBiRkZHif927d/fVKRIRERERAXAs9VXJ20ePwsaWHtcabRWFIRplm5xTU64ek4Ldj03HbZMzPO7nMBMiIqLA5bN3RsuWLUNtbS3Wrl2Lxx57DKtXr0ZVVRU++ugjaDQap2ODgoJQV1fn8X4WLFgArVYr/pefn++rUyQiIiIiAiCtKGwnPQotnpfpFmn1eOi7vQCAME37qCgEgOhQtdd9KrFHIZceExERBRqfBYWnTp3CmDFjEBMTAwBQKpUYPHgw9Ho9SktLnY7V6XRQqz2/udBoNIiIiHD6j4iIiIjIl+whltLfFYUuw0y25ZZj3OI/sTKrCADwyI/7xWND1O2jorAp9h6FDAqJiIgCj8/eGXXv3h319fVO206cOIEXX3wRW7duFbfl5eXBYDCIgSIRERERUVvQmyxYmXUa2nqTuCzW31OPFS7LdG/9LBOntXrc8cVOAEBeWa14bHvpUdgUe/ja2HJqIiIiap98FhTOmTMHBw8exDvvvINTp07htddew549ezBjxgxotVp89tlnAIAlS5Zg2rRpUCgC440OEREREXUML64+jDu+2IU7Pt/pWHqs8G9FocqlorBabxb3FVTV40SFo11PaDvpUdgUe/jqbTk1ERERtV8+e7cRExODlStX4sEHH8QDDzyAxMREfP311+jZsyfee+89XH311Zg/fz4sFgvWr1/vq4clIiIiImqWr3fYel9vyS1HRnwoAMfgDX9RiEGhe6g2Yclap+uBEhQqFZx6TEREFKh8+m5j7Nix2Lx5s9v2efPmIScnB5mZmRg/fjzi4+N9+bBERERERC1yrNS2pNde0ecv9opGSzOW6YYE3NJjVhQSEREFmjb7WjIpKQlJSUlt9XBERERERABsvQk1Ss+Vgyov29uKvaLQZLFi3eGSRo8NlIpCx9JjVhQSEREFmsB4t0FEREREdAbyK+ow4+UNmDukq8f9Sr9XFNoe32IVcLS4xq/n4itKceoxg0IiIqJA49+vUImIiIiIWtGk59ah3mTBt5mnPO739zAThdwRqhmbGP4RF6Zui1M6a6pG+i4SERFR+8agkIiIiIg6JNe+fzrJRGE7fweF9sEfZqsVRrP3YO2iYUmY2iehrU7rrLCikIiIKHAxKCQiIiKiDmlDTmmTxyj8vPTYvvTZbBFgaqgojAxWOR0zoFsEXr5iKGQy/55rczmmHrOikIiIKNCwRyERERERdUjL955223bj+FQM7xGNe5buBmAbdOJPjgnBglhRKB288tWtYzC0e5Q/Tu2MqRsqCs0MComIiAIOKwqJiIiIqEM6Wuo+HOSuqT3xjyHdkBwdjBC1AhnxYX44MwfHhGCrWIEnXTI9PiMOIerA+m7fXiVpsnLpMRERUaAJrHcdRERERETNVKYzAAA+vmkUdp+ohFopR3y4BgCw9sFzYLJYEaxW+PMUxR6J0mEmFw9Pwl+HSzG9fxd/ntoZU7KikIiIKGAxKCQiIiKiDqnOaBtekhwV7DYIRK2UQ630/+IaaT8/o9lWgRcXpsEfD0zx52mdFUeVJCsKiYiIAo3/3x0REREREbWCWqOt/6C/qwYbI/bzszoqCv09ifls2fsucukxERFR4AnsdyFERERERB5YJMNB2nOPP/syXZPZCqPZFmy2h0rHs6GU9F0kIiKiwBLY70KIiIiIqF3KKtBi54lKvz2+fdkxAIS044pC+zJdk9UKU8NS3UAPClVij0JWFBIREQWawH4XQkRERETthtlixc4TFdCbLLjg9U245O2/UaLT++Vc6hqWHctlgKYdB2/2UE1vsmLtoRIAjuXIgcox9ZgVhURERIEmsN+FEBEREVG78cLqI7jk7S146Lu94rY9J6ta5bFMkmWt64+U4pzn1yH14d/w0urDABxBYYhaCZlM1irn4Aue+hG2556KzcGKQiIiosDFoJCIiIiIfOKd9ccAAMv3nRa3legMPn+cX/cWotejK/B/3+9DWY0BN3y0HXnldQCA19YeRUFVvbj0uD0vOwYc/fyk4sM1fjgT35FOciYiIqLA0n47OxMRERFRwPP1QAuTxYq7l+4GAHyTmY9vMvPdjtmXX4W4hrCtvQeFnpYZx4cFdlBoryhkUEhERBR4GBQSERERUasxncHy010nK/FdZj7mz+yLmFC10747v9jZ5O0/3ZKHAwXVAIDgdjzxGHD085MK9IpC+4AWs5VLj4mIiAJN+37nREREREQB7UwGWlz81t8AAKsV+N+lgwEAgiDgpz0FWHOwxONt+nWNwLl94/HmumPYmlshbvcUxLUnCpfze/XKoQhSte8qyKYo5exRSEREFKgYFBIRERHRWRMEz6GQySzAahUgP4PA7lSVre9gYVU9xi9Z6/GYnQun4bU/c3DZyO4o1RkAHHPaf1pb3+LHbUvSQSvBKgUuHJrkx7PxDfYoJCIiClwcZkJEREREZ2XniQqsOlDscd/La45g1LNrUKLTt/h+o0LU0JssbiHhvef1Ei/Hhmnw5IUDMTApEhN6xqFrZJDTsWU1xhY/rr/EhqmbPigAiFOPufSYiIgo4LCikIiIiIjOmNlixSVvb2n0mPJaIz7enIf/m9W3yfuTViZGBqtw48fbnfa/f/1ITOuXgKgQFQYnRzrtUyvl+OCGkfh2Rz4+3XKiBT9F++C6DDlQ2Zd7V9QaceEbm/D5LWMQEaTyeOzfx8qgUcoxokdMW54iERERecGKQiIiIiI6Y9V6c7OOa24EZpQsV1Ur5E79Bp+9aCCm9+8CmUyGmyakeQyXBnSLxJMXDsRnN4+GWinH/y4Z1MxH9p9bJqYBAObP7OPnM/ENlWSS895TWnyyOc/jcfVGC65+fxsueXsLquoCp/KTiIioI2NFIRERERGdMW29qVnHNbdaTm90BIWlNQbx8sCkCFwyPLnZ5zW5dzyyn5wJpaL9fy/+8Pl9cfmo7uiVEObvU/EJe49Cu3qTxeNx1XrHc2dHXiWm9+/SqudFRERETWNQSERERERnrLmVYM0OCs2OUOl0lW0QSfeYYCy/e1KLzy0QQkLAdp69u4T7+zR8xj712M7LnBvoJQFiraF5lalERETUugLj3RMRERERtUtVXioKXYNBhax5QWG90REe7TpZBQCICekYQz46C7VLQCvAc1KoNzmqR41mTkgmIiJqDxgUEhEREXVyK/afxlfbTp7RbQsq6z1ut7hMvJU3s6LQ0zJVtZJvWQNJc39f0opCg4VBIRERUXvApcdEREREnVhOsQ53frkLADCtXwISIoI8Hme1CpDLZbBYBbFa8NEf9+NLLwFjiFqBOkl1YLOXHnsICmsMnnvcUfukculR6KWg0CkUNrGikIiIqF3g17NEREREndix0lrxss6lT5xOb4LZYsXCn/Zj9KI1WPT7QfR69Hf8fawMADyGhP26RmDhnH6Y3CveaXszc0KPFYXXjk1p3o2pXVAq5E6/b085od5kweM/Z4nXjawoJCIiahdYUUhERETUidVIwkGTJKw5VFSNua9vQveYEOQ2hInvbcgFALy4+ggG3RTp8f7O6ROPWyalY1q/Wqw8UCRuN1u9lJW5cK0oXHHvJPTrGtG8H4baDZVCDkNDlaDgYZrJ+xtycaS4RrzOHoVERETtAysKiYiIiDoxnd4xjMRktgU62joTZr2yESaLIIaEUmargOJqvcf7sw+ySI0LRe6i2W733fT5OFc19ulA04A7E+lSc09Tj3PLnJ9XDAqJiIjaBwaFRERERJ1YjSSYsy//3JJb1uhtdPUm/Li7QLz+z4lp4mXpIAu5XIYbx6cCcK5WbExVnfMU5eYOQaH2Rfpba05E3NznBxEREbUuBoVEREREnZi0L6HRbIW23oRHfnT0juseE+x2m9yyWry57hgAYFKvOEzqFSfu07hMvLUHh80NgrT1jqDw+UsHN+s21P7IZI1XFLoysKKQiIioXWCPQiIiIqJOTLqE2GSxYuhTq8Vg5/bJ6Vgwux+q9SbMeW0j8ivq3W4fG6pGcrQjTFS7BIX2CbjNHVbxbWa+7bGnpOOykd1b9LNQ+yR4qCl0rRPlMBMiIqL2gRWFRERERJ1Uqc6An/cUitdPVNQ5VX8lNQSAEUEqrLh3Mn7990S3+5g1MBFJUSHi9Xqj8zCSUI3te+nKWmOzzulUZb34mBS4pEFgc6pJTawoJCIiahdYUUhERETUSf26t9Dp+r78KqfrSVGOSsEwjRL9uroPFpk1sKvT9bIag9P1nvFhAIDDkgm33kgnHl82MrnJ4ykwNGdQCSsKiYiI2gdWFBIRERF1QiU6PSrrnKv89p3SipeVchn6JDoHg0qFHPdP6w0AiA/X4NDTs9zuN1jt/D109xhbtWGpzuB2rCt7yKhWyhEfpmnGT0HtlqSk0GRpukkhpx4TERG1D6woJCIiImoHTBYrlHKZ0xCI1rIxpxTXfbjdbfvhYh0A4JLhybh5YiqSo0Pcjrl3Wi/MG9YNQSoFglQKcfs7147AL3sL8M8JaU7H24ebGM3OS5I9KauxBZdxoeo2+XOg1iP97XkMAV1+vQwKiYiI2gcGhURERER+VlZjwHkvrseo1Bi8f/2IVgnJKmqN+PTvPMwbloTrP3IPCaWGdI/EgG6RXvf3iA112zZrYCJmDUx0224fbtKcqbaFVbb+hF0ig5o8lto36XPY0+9e5pIUcukxERFR+8CgkIiIiMjP9hdooa03Yc3BYpwor0NqnHsQd7YWLNuHVQeK8eqfOU0ee7kPpw1rlLaqQ6PFCkEQGg1BF684CADo7qGSkQJXc0JAVhQSERG1DwwKiYiIiPxMpzeLl2sM5kaOPDOCIGDVgWKnbU/M7Y+rx6Sg1mDBW+uO4oNNxwEAN45PdVpSfLbsFYWCYOtVp1Z6DwrrDLblyZHBnHgc6KR5sKeJxlbBuW8hKwqJiIjaBw4zISIiIvKzMsmgj9YITFZnF7ttG5gUCY1SgZhQNc4f5JhcHB7k2++R7T0KgaZ/NvvU4ytH+66ikfzP0+/dXkE4tU+803UiIiLyLwaFRERERH4kCAKeWp4tXm+NwOStv445XVcr5OiVECZeH5zs6EdYpNX79LHVCklQ2MjPJgiC2MsuJlTt03OgttfUMBP77zo8yFY9amJFIRERUbvApcdEREREfpRbVut03VdBoSAIsApAndGMfaeqAAA/3DkONQYLEiOCEBXiCONUCjmiQlSoqjNhZGq0Tx7fTi6XQaWQwWQRYGhk8rHRYoXZaluOGqrhW9SOxFMIaK8yDGuoYGVFIRERUfvAd2FEREREflRRa3S63pzpwM1x99Ld2H2yCh/eOBKCAISoFRjRI8br8SvunYStueW4YHA3nzy+lFohh8li8RoGHS2pwa97C8XrIT7skUj+IR1a47GisGGZeZiGQSEREVF7wqCQiIiIyI+2Hit3uu6rwGT5vtMAgFmvbAQAhKgbf9vXNTIYFw1L9slju9KoFKg1eg8Kp7203um6UsHuOIFOuvTYU/gtVhTag0IuPSYiImoX+C6MiIiIyI9e/OOI03Wjxfvy3OayWAW3bfYKLn+w9yn0FBjp/Xhe1Hqcph43MswknEuPiYiI2hUGhURERER+Umc0u23zRWCi05vcN8rcN7UVjcp7UJhb6tyj8el5A9vknKjtNDb1OJQVhURERO1KqwSFs2bNwieffAIAyMrKwqhRoxAdHY358+dDENy/4SYiIiLqjE5V1rtt80WPQm29e1Doz7dgjopC9+rBo6U14uWM+FBcN7ZHm50XtaYmehTaKwrZo5CIiKhd8XlQ+OWXX2LVqlUAAIPBgLlz52LEiBHIzMxEdna2GCASERERdXZPL89223Y2gYnFKkBbZ8Luk1Ue9/mLWml7y+npZ9uUUwoAmN6/C366a0Kbnhe1nn5dw8XLnn7v9m32qcdWwb/PUSIiIrLxaVBYUVGBBx98EH369AEArFixAlqtFi+99BIyMjKwaNEifPjhh43eh8FgQHV1tdN/RERERB3Rxpwy8fLlI22DRM60orBUZ8Ajy/ZjyFOrcd83e9z2K+X+W3us8RAUCoKAFftP49vMUwCA0akxCA9S+eX8yPdevGwIZvTvAgAwWwVYXUJA+1Jj6e+cVYVERET+59Opxw8++CAuuugi1NfbltHs3bsXY8eORUhICABg8ODByM52/+ZcavHixXjyySd9eVpERERE7U6pziBevmxEcqNVd64EQcC/l+4GBOCNq4fhWGmt2+RgV6PSYs7uhM+C/Wezh6B/HyvDPUt3o6zGKB4zICnCL+dGrSMhIggvXj4Eg/67GoAtGAySK8T9YkWhxnlbsFoBIiIi8h+fVRSuW7cOf/75J/73v/+J26qrq5GWliZel8lkUCgUqKys9Ho/CxYsgFarFf/Lz8/31SkSERERtRtbcssB2Pr3PT1vINQKW0DSnKEOJToDftt3Gr/tP438inpsbbgvV7MGJAIAkqKCsfjiQT4685ZTKxt+toZw6J6le5xCQgAYnhLd5udFrcseEAPuz2v7tGv7MBMAMPhg4jcRERGdHZ9UFOr1etx+++14++23ERHh+DZYqVRCo9E4HRsUFIS6ujpER3t+M6jRaNxuQ0RERNTR1BpsE48n945HkErhqLozNR0Ubj9eIV7ekVeBYw0DQdRKOR6d3Q9P/HIAE3vG4Z3rRrTCmbecuPS4ISyqMTgPW/nujnEIUrGSrKNRyR1BoUlSKWu2WGFuWIocpLQ9941mK0wW9igkIiLyN58EhU8//TRGjRqFOXPmOG2PiYlBVlaW0zadTge1Wu2LhyUiIiIKWPbqOnuI5gjTGq+qslgF3L10t3j9we/2ipef/McAXD6yO/p3i8CAbu1nKa8jBLX9bBFBKuhNjqXXydHBfjkval1yuQwqhQwmi+BUUSjtwxmkUkCjsAWF7FFIRETkfz4JCr/66iuUlpYiKioKAFBXV4dvv/0WqampMJkc3xjn5eXBYDAgJsZ/PXKIiIiobfx9rAyFVXpcOiLZ36fSJj7YmIutuRV465rhTksuvbGHIvZjG+tRqDdZcP1H23FaW4++id4DwMtGJEMhl2FUavt6r6VROFcUuvahS4wIavNzorahVshhslicntf2ZceALSBXK+WAgcNMiIiI2gOfBIUbN26E2WwWrz/00EMYO3YsbrzxRvTv3x+fffYZrr/+eixZsgTTpk2DQsGlJURERB3d1e9vAwD07xqB/u2ouq21PPPbQQDAiqzTuHBoUpPH20MztcKlotBDWPLSH0fE5cb5FfXi9mcvGohHf3Ss3lAqfNZ+2qc0KseyakEQUFytF/elxIRAJvPfRGZqXSqlHDBaYPJQUahWyBuqDps/yIeIiIhal0+CwuRk50qBsLAwxMXFIS4uDu+99x6uvvpqzJ8/HxaLBevXNz6Rj4iIiDqWU5V1HT4otFodvdWk1VKNMXipKNR76FG45Zj7sJJvbx+H0WkxMJqtePLXbCzx47CSpqglFYXV9WbxZ5wzuCsentXXn6dGrcz+uzd4qCi0B8hqlx6WRERE5D8+CQpdffLJJ+LlefPmIScnB5mZmRg/fjzi4+Nb4yGJiIioHREER3Bm7QTzCarqHa1WHl62H//3w35M6hWHz24eLVbLmSxWyABU1pnwwLd7sDGnDIAkKGwIVFYeKILZYhWrA00WK/YXaAEAcwZ1xbHSGgxLicKIHrbBcDeOT8XMAYntevmuRuWYelxZZ5t2HKpW4M2rh/vztKgNiNWkZveKQk3DNOzGlt0TERFR22qVoNBVUlISkpKaXoJDREREHYPZKR3s+Enh0ZIa8bI9I92YU4Ytx8oxMjUGr6w5grf+OubxtvaQRLo082RFHdLjwwAAf2QXi9v/d+lghGmc377JZDJ0i2rfw0CkVWX1DdVkweo2eRtKfhbUEAZKK23tl4NUzsvu9ebmVeMSERFR62mfjWyIiIgooEkrg4SOnxPix92nPG6/+oNt+Hjzca8hIeAI0Up1jinA0p59BZW2noRRISq3kDBQiFOPzVbUGW1hUIiaPas7A2l/SjuDy8Rv+/O61mAGERER+ReDQiIiIvI5aVBo6QRJYWZepdd9H2/Oa/S29hAtQbJ02CCprCqrsQWIlwwP3OnR0kEt9mqyYBWDws6g8YpC277wIFtQWKNnUEhERORvDAqJiIioxYqr9fh5TwHMXoYPSPuRmTrBgILyWlvfvRn9u+CpCwc47SuSTPgFgKl94rH54XPF6/YQ7bKRjiBQWn1V2hAUxoVpfHvSbchRUWgRKwqDWVHYKdjDQOmyYvswG/tzP7ShojCvvK6Nz46IiIhcMSgkIiKiFlv8+0Hc+/UePPpjlsf90opCg4cpvh2JxSqIAzqeuWggrh+XiqddwkKpR+f0Q3SISrxu7+eoUSqQHh8KwDlorW4YlBIluU2gkQ6rqGdFYacS5HHpsXNFoX3p8Tvrj6FEpwcRERH5D4NCIiIiarGf9hQCAL7JzMemhum9UkaL+zLDjiivrBbPrTwk9mGMCVEDACb0jPN4/I3jU9EzIRzBKgUGdItATKgac4d0E/drPCzTrG5YjmlfnhmI7D+XwWxFvdH287BHYedgn3gtfU7bQ0N7UKiQO3pyHjyta8OzIyIiIleB+46TiIiI2oVrP9yGvCVznLYVaR2DOfTmjldRmF9Rh482H3fqPxgRpISyYTCJtynEPWJDANiGlfx01wRYBUEM0QDHUszME5WY3DsegKNvW6AOMgEcFYWVdUYs33caABDEoLBTEHsUmt0rCu3P94k94/DZlhMAAKET9DQlIiJqz1hRSERERC3y8h9Hmjzmxo+3i5c7YkXhf77f5zakpFoyiCFIpcDg5EgAQGyoGl/dMgY3TUjFRcOSxGNUCrlTSAjYljEDwGt/5oh/bjWGjlBRaHvLue+UFhsbKlDjQtX+PCVqI/alx87DTJwrCmcMSBT/vth7WBIREZF/BO47TiIiImpzgiDg1T9zPG6XyRzLB+199wDnfnsdxZbc8iaPeeWKoViRVYSrR6cgOlSN8V6WI0sVVNWLl09V1qFnQrgkKAz8HoVS3WNC/HAm1NYcy+kdrwP20FAjeV5ENSzbZ1BIRETkX6woJCIiomarrjd73J624HeU6gwo0uohCAIkLcc6ZEVhTDOq4dLjw3DX1J6IbkHlXHpcqHj5RHkdKmuN0DYMM+kIFYVS147t4YczobbmqaLQ/uVBkGSgTUjDZXsPSyIiIvKPwH3HSURERG2utMbRe/DcvglYe6hEvD7q2TUeb6MPoKnHB09X4+2/juGB6b2RKgntpKr1JlTU2qYc3zU1A+f0ScAjy/bjgem9z/rxb5mUhswTlQCAkxV12HtKC4tVQN/EcCRGBJ31/ftLfJjG6fpXt4xxComo47L/nu19CQFJRaHKESCHaGzHsaKQiIjIvxgUEhERUbOV6mxBYXpcKN69bgR6PbqiydsYAqii8KaPd6CoWo8jxTqsvG+yx2NOltcBAOLC1Jg/sy8A4I8Hpvjk8WcN7IrbJqfjvQ25ePLXbHH7vGFJTku7A01Xl+EuoQE8mIVaxl5RaDBJh5nYLkt7dNqnYNcaWFFIRETkT1x6TERERM1W1lBRGBeugUohx4p7JzVZ6RZIPQqLqvUAgENFOq/HVDcsBY4OaZ1hHAO6RbhtC/RgLUyjRKxkCXag/zzUfPaKQr2HisIgSUVh10hbmHyyoq4Nz46IiIhcMSgkIr/ZlluOK97dgsONfCAnovbFXlFoX0rar2sENvxnaqO3CdQehVuOOQaWrMw6jQ825sJqFcTpxq3VM3D2oK5u20I6wDLda8akiJfDGBR2GkEehpl4qijs3SUcAJBTUtOGZ0dERESu+C6NiPzmive2AgBu/zwTf81vPGggovbBXu0TF+aoDvM00VZKWknU1k5r6/HM8oO4fUo6BidHtei2V72/FQ/N6I2ZAxJxxxe7AADP/HZQ3G+RTHb2JZXC/c8zWB34QeG143pgQ04ZesSGoEuEpukbUIeg8TDMxFNFoX1AkE7PpcdERET+xIpCIvK701q9v0+BiJpp/ZFSAEB/D8tj7eYMdq6IM/hxmMmLq4/gt/2n8Y83NuNoE5VKOr3JbdsLq49g+ssbPB6/95TWJ+foyUaXKs3gDlBRmBAehJ/umoBXrxwW0P0WqWU0YkWhJCj0UFFof47XB2gFMhERUUfBoJCI/EIQHJU4cn5gJAoYZQ1Lj0elxjhtv21yOgDgjikZWDRvEBbO6YfnLhkMwL8VhdL+iF9vP9nosUUt/NJCKW+9167uMSEYkhwpXu8IFYXUOQWJFYWSpcceKgrtz/FAbVVARETUUTAoJCK/2HzU0ftL0YoftonINypqjSivMaDGaO/Pp3Laf+95vfDlLWPwf7P6IDJEhVsmpSM5xjacQO/HikKNZFl0TRPTVO3VzX26hOPlK4Y0ed/f3zn+7E6uCQ/N7CNe7ggVhdQ52YeZGMzuFYVBHioKGRQSERH5F3sUEpFfnKp0TDVkTEjUvulNFsx6ZQMqao2wFwO7DvII1SgxoWec07agVv7gX1hVj5MVdRibHuv1GG29YzlxU0saT2vrAQBdo4Jw0bBkZMSHQQYZ5r6xCQAwoWcsPrlpNO79ejdSYkIxtHvU2f8QjciIDxMvx4S2zoRlotbmeB1wryjUSCoK7dWFJosAk8XqsVcnERERtT4GhUTkF9JqpDpWDxC1a/kVdShpWHIMAGqFXPzw3xh7tZB0+a+v5JbW4NwX1wMAlv1rPIanRHs8TlsnCQqNnl9rtPUmWK0CDjVMYO8aGQQA4vCTvCVzsOtkJZKjg6FSyPHWNSN89WM0qltUML6+bSxOa+vRPSakTR6TyNfsAaC0otD+miB9HZFe1pssDAqJiIj8hEEhEfmFtC1ha00OJSLfOFVV73TdtZrQmyAP00595T/f7xMvb80t9xgUCoKAIyU68bqnikKzxYo5r21EvdGCuoYgcVxGnNtx3oLI1tZYtSRRIHAMM3F8YWB/TZC2BtAo5ZDJAEGw/V11bW/gicFsgVoh53AcIiIiH+JXdUTUJgRBwF+HS8RhAdLKgsGShv1E1P7kFOucrg9p5pJbjb03WSv0KLRX/wHeByJtyClDlaSi0FNgmVdeh1OV9SivNYpBYv+u4T4+W6LOy9MXBp4qCmUymdinsKnXjMNFOsx5bSP6LFyJJSsO+fqUiYiIOjUGhUTUJtYcLMGNH+/ApOfWAnCuLDBbWFFI1J7tPlnldL13l+YFaUEN1UJGixV6kwXXf7QdL/1x5KzPZ/m+QqfBJLVehpQcKNQCAMI1tgpITxWFh4t0btviwjRnfY5EZGNvQWC2CjBbrDhaokNFrRGAc0Uh4Bho0lQ/0Zf/OIIDhdUAgHc35MJs8d/AJCIioo6GQSERtYkNR0oB2JqUA86VBXozexQStWeulXhBqua9fZBWC/V9bCU2HCnFa3/mnPX5fLDxuNN1nd5zUPjXIdvrTu9EW7DpqUfh4aJqt20RzVjySETN49R70GzFLZ9metwnve6tn6hdblmN0/XaJo4nIiKi5mNQSERtQoBz1eCnf+eJl/V8g0/UrrkW/TZnkAngXi10Jk6U1+KVNUechpKUSgarAECd0T0oNJqt2J5XAQBIigoG4Khk3n2yEiOfWYNnlmfjaKlz4DA8JQpyOfudEfmK9HXAYLIgr7xOvG6vNhSvN3wJ0VRFoeuApG255Wd7mkRERNSAQSERtbl9p6qcPig09YGAiPzL6jJwKKiZAaDSy9RS1/trzJTn/8Ira3Iw5KnVEAQBuaU1KGgYrjK5dzwAoN5DP7OyGkeYOGtgYsNxtteaZbsKUFZjwAebjuP3/UUAgHeuHY7Dz8zCsn9NaPa5EVHT5HIZ1A2vGXqXgE/jUp0crG7e0uPyGqPT9ds+3wlBYBsTIiIiX2BQSERtQvr+Pb/CeYIqg0Ki9s11MnlzKwoB4Im5/d22eWs3UFKth0nSayyzoSLQ7qnl2XhljWPp8tj0GACelyn+ddi27LhLhAaDkiKdjvM01CQ5OkSczkpEviX2HnSp/vXWo9DQyPsCQRBQ66GKWN8KQ5OIiIg6IwaFRNQmpDGD2er8Zl5vsraowoiI2pbFpVLHXvXTHDdNSMPWBec5bXMN9kp1BnyXmY/Ri/7EE78cELfvOlnpdNzHm/Pwy95CALYBJfYlxae1zl8+AMAjP+4X7ztIMiBBEAQUuyxdBpo/oIWIWi604TWjzuXvvsxlYnlQM4aZ7DpZBU/Fg5V1RveNRERE1GIMComozXmq5nHtN0RE7YdrRaFS3rK3D4mRQXj/+pHidWkI8NW2kxj17BrM/36feN0e/J2sqIM3b14zXAwVDhRWY/WBIpgtVvy+/zQqax2BwT+GdHMKNg1mK0qq9QCAxy/oj1evHIojz5wvLo0kIt+z/x2sNTS+gsBReej9PcEtn+7wuJ1BIRERkW8o/X0CRNQ5SL/99zShVG+ytKhKiYjajmtQqK03eTnSu+n9uyA6RIXKOpNYUfjehmNY9Psht2PHLV6LuDCNU59BV9EhatRLKgl/3luIh77bi2q9GRcO7SZuX3TxIKglvRJ/338ah4p0AIAJPePQJ5GVhEStLVRj+8hRb/I8odyuORWFlXWeX3+qvGwnIiKiluHX50TUJqok3/TbBxFI1Rga//BARP5jbUj6BydHoldCGM5vGA7SUvZqoVqjBesOlXgMCe3sIaFaKcfG/0x12x8bpoZcsmzxQIEW1Q1fQvy8p1DcHqRUQKmQi2HhA9/uFfd1idCc0c9BRC0TIqkoHNAtAgAwqVec23H2QLHGwxeKnkgrgfk+goiIyDcYFBJRmyiUhIN/ZBe77S/x0DOMiNoHe0XhA9N7448HpiA6VH1G99OtoafgS38cwfe7TonbX7tqGN6+ZjiOPHO+222uGNkd3WNCcPOENHHb9eN6oFtUMKb2iceQZNugEukkdbtglQJyuS1MVCpkbvsjg1Vn9HMQUcuEqm0BYJ3RLA4sunNKhttx0SG2v5PelhG7Tjb+/Z5J4uU6DwNOiIiIqOUYFBJRmyhq6AkGAKcq3SsKL3n7bxwt0bXlKVEAqKoz4qSHAIjalj0oVMjdw7aWGJYSBQDYcKQUv+07DQD48IaR+MeQbjh/UFeolXKsfXAKrh/XA+f1TcC0fgm4aUIqAODuc3tiTFoM7p/WG09dOBAAoFTI8fVt47w+nnT5Yo/YUKd9Ox6d5jZIgYhah7RHof3vZZCHdiPRIbYvIaq8BIXSycYPn98XPRPCMHNAFwDug1KIiIjozLBHIRG1KoPZglKdwWvvoNFpMdh+vAIA8PTyg/j05tFteXrUzo1d/Cf0Jiu2LDgXXSOD/X06nZZ96bHiLIO1f53TE6sOFDsNKUkID3I6Jj0+TAwCpaJD1fjmdvdQMFitwLCUKOw+WdXoY7959TB88nceenUJx7j0WMSHc9kxUVuxVxTWmyzioBJ7KwKpyIaKQnsfUVfrj5SKl2+blO5033VNDEohIiKi5mFFIRG1qus/3I6J/1vncarx7semIyUmRLxu5ORjkjBbrGL1yM4TlX4+m87NXlEoP8uKwuhQNZ6e5xwCxoWf2TJmqZcuH4qEcA2Gdo/ChJ6xHo+xB5DXje2BnglhZ/2YRNR8IRp7RaEZ+oaKQk9Bob2i8FCRDmsPubcpueOLneJl++uRWK3IpcdEREQ+waCQiFrVtoZqQVdqpRxRISqESpYeWV16D1HnVlbjWHr27692ix8uqe35aukxAHGQgV1s6NlX9qXFhWL7o9Pw010TsPiiweL28CAunCBqD+xVf7UGs7j0ONjj0mNH31DXYUdaLysT7ANQXlmTI/Y/JCIiojPHoJCI/CIhXAOZTIYQjeODPGNCkpL2tQSAA4VaP50JWRpCfLkPevrFhTkHg9Kppb6QEhuC1fdPxhUju+PXf0/06X0T0Zmxh4LaepP4xUOQh4rCqBBHhXGtyxTjslrH0LMHpvcWL4dIAsdvduT75oSJiIg6MQaFRNRqXN/kS3WJsPUlk1YUMikkqSKt+9AbTwRBwNGSGlitfAK1FmtDkY7SBxWFgCMcHJ0W45P7c9W7Szj+d+lgpMaFNn0wEbU6+7/15bWOSvEglfvHkChJRaHrewh7D8LEiCDcc14vyX07vnDkF0pERERnj0EhEbWagirvQU+XCFtVUbDkDf72vAqvkw6p8ynSOlcUeltR9vb6Y5j20nq88mdOG5xV5+TLpccA8MlNo3D+wES8ftUwn9wfEbVv9tUDFQ1BoVwGqBUegsJgR1Do+tVPXUMPQnu/Q8d9O64XVDn/u0FEREQtx6CQiFrN8bJap+vvXjdCvNw30danLMSlR9Gb6462/olRQDjtsvTYW6P651YeBgC81smDwiKtHo/9lNVoQH+mfLn0GADGZ8Th7WtHiJXFRNSx2av+ympsy4eDVQrIPLyeKBVy3D7FNs3YNUisM9oqCl3fNwQpJUFhZR2IiIjo7DAoJCKfEQRB/MYfANYdKhEvv3zFEJzXNwGzByUiSCXHef0SAAAxoc4TTytqPTcrp86n2KWi0L7sjJyZLFacqqzDf37Yh8+3nsDl72zx+WNYfVxRSESdi73qr1RnCwpDNd4HDf1zYhoAoKLOKFYzA9Kg0Pm20rxRW8/Jx0RERGeL4wCJyGce+m4ffttfiFX3TUa3qGB83dBU/MMbRuK8fl0AAG9dMwImixWqhkqBqX0SMDY9BltzbdORBU4+7pTqjRboDCYkhDsqzE5rm1dR2BnkV9ShtMaA4SnR4raCqnpk5lXg3q/3OB3r64pCg9ki9hXzsFKQiKhJIQ2DS+y5X2NBYUzDQBNBsL322XuN2v8NCHWpKJQGhRYrpx4TERGdLb7lJyKfsFgF/LDrFPQmK15dk4PeC1eI+/p1jXA6ViVJG9RKOf491dGU3MqgsNOxWgVc++E2TPzfOuRX2JaN5VfUYdvxCqfjavSdMyg0mC2Y9Nw6XPzW38hrWM5fUFWPqc//5RYS2mnrfFeZ+/Zfx8TLvlp6TESdi2sw6Lp8WEopeY9w0yc7xMt1DcNN3CoK4XhdqjU2Xnleb7Q4VSkSERGROwaFRNQsu05W4sI3NmFPfpXH/ackfYGW7S6ANO/rGtl4HzJpI3IL3793OjklNdh5ohJGsxULlu3Hl9tOYNJz6wAAydHBGJhkC5qr6k04ra3HeS/+hf/+cgBmixVfbTvpz1NvdTnFOgz+72rx+uFiHYxmK5785QCM3qa7AMj3YZ+uzUfLxMtcekxEZ8I1GGysolBK2uu4zuS5R2HPhDDxstFshdHs+bVRW2fCiGf+wNXvb23WYxMREXVWDAqJqFnu/2YP9p7SYt6bmz3u/2zLCY/bN8yf6rFhuZS0YTkrCjufkxWOUGvT0TI8+mOWeD0xIghT+9j6WVbWGjH39U04VlqLT/7Ow2t/5uCRH/e3+fk2R2FVPb7YesKpZ2dLVetNePznAzBIPvRarQIWrziI1dnFjd62xuCb6kuTxYodeZXidVYUEtGZcA0GXZcPN4e9T63rfQ1MisQbVzsmqH+wKdfj7dcdLkGd0YJtxytQazCj1kevk0RERB0Ng0KiTubbzHw8v+pQi3sBFrtMoJXamFOKDzcd97gvJTakRY9j5ZKgTkEQBPz3lwO4/5s9ThVrrh6d0w9RDf2qPt96AmU1RnGfvQemlLdKkrZksQqY9+ZmLPwpC/0fX4Vznl+HU5V1OFykwwPf7HGbBu7N/O/2YktuudO24mo9Pt6cJ16/YVwPvHPtCLhq6TLtzLwKPPFzFh76bi9MDZWKgiDgP9/vczqOFYVEdCaCXYLBkCYqChddNAgA0DcxXNxm71Hoel8AcMHgbuLll1Yf8XifJkkV9tw3NmHqC3/ht32nsdXldZaIiKiz4zATok7k172F4gf/8wd2xcCkyGbftrEA5vU/j3rc/vSFA5p1330kHwT0Jk627Qz2ndLik7/zGj1m4Zx+GJYSjbxyz8FaScP0TKlagxlqpdrD0W3nn5/ucDq3vPI6nPfierEycNnuAmQ/NdOtz5bdkWId3lx3FKsOuFcN2of+ALbldzdOSEOoxv1Dc2mN+5+NJ4Ig4Kr3tzrd7/CUaFw9JgVPLz+IH3cXOB3PoJCIzkSoy+udfbiJN3272t4XSKuj6xv6DzZVjTgqNUa8XGswY0deBcZnxMEk6W2SW2r7d+Wur3ZBpZDhr/lTkRQV3IyfhIiIqOPzWUXhzz//jPT0dCiVSowZMwYHDx4EAGRlZWHUqFGIjo7G/PnzOdGUqBXlFOvEN9KujhTrcPfS3eJ1QwsqrypqjZAW+km/lf9x9ylsz7OFDN1jnN9kZ0j6BjVGpZDjtatsy4Z8tWSS2q8T5bV4YfXhRo9577oRuGVSOgBgZI+YRo+Vaovnz4nyWhwt0Xncd7SkBn8dLnXb7vr37fJ3t+BAoRYAUFVnxFXvbcXAJ1bhoe/2YsbLG/DznkLx2HeuHYG7z+0JAFh5oAgAkB4fil2PTUdaXCgSwoNwx5QMdIsMwpBkW/i/YNl+r68FdnVGM+7/Zo9TSAgAj/y4H3cv3Y2PNrtXCZtZ8UtEZ0Ahl+Ge8xyDy5rqYhARZAsWdZLqaHsVubcvWa4ZkwIASJT0RX7w27248eMdeHf9Ma+tIEwWAXu99F8mIiLqjHwSFB47dgw33XQTlixZgoKCAvTo0QO33HILDAYD5s6dixEjRiAzMxPZ2dn45JNPfPGQRB2e2WL1Wl1nsQr4dW8hVmadRkm1HtV6EzbllGH6yxtws2RCoF1eWS0ue2eL07amQgSp//vBefmhtt4xUfXH3Y5A477zejsdF65RNfsxooJtx+o66WTbzmLz0TJMef4vbMxxXm6slFSqxYWpMWNAoni9e0wI/m9WXwDAHVMycNOEVK/339gS+eY6WV6H2z/PxMHT1W77tuWWY8rzf+Efb2xGtd59svC3mbYPsuf2TcCex6fjwxtG4oXLhrgdl1VQjTmvbcLKrCJ8vDkPW3LLUWMw4/udp5yOu3F8KmYNTMRtk9OREuNYxp8UFYwgSUXOw+f3xd8LzkN8uOMDsrT3o1SJTo+jJTr0f3wVfpIEklK/7vW8PayZAwiIiFzdP80RFDb1nUNYw/uHGoMZgiBAJ3m99fY+YUC3yIb9jmPtX658tPk4qhqZBq/z8HpORETUWfnkHf/BgwexaNEiXH755QCAO++8E7NmzcKKFSug1Wrx0ksvISQkBIsWLcJdd92Fm266yet9GQwGGAyOJVPV1e4f1Dqzz7eewFvrjuK960ZiUHLzl41SYPk2M19cIrzp/6YiOdq5z9+NH293Clp6JoQhtaEXoGtPMwB44pcDTuEeAHy+NQ89E8Kcvnn35mS5c+BQpNUjLkwDAChpCGbuPCcDl4xIxpt/HUVuaS0igpRIjw9t8r7twhqqB1hR2LG9t8Fzk/mrx6SIA3FG9Ih223/nORm485wMALZl8NI+fVKrDhRhZGrzKxA9WfDjPmw+Wo5VB4qRt2SO075FKw4BAOqMFgz+72psf+Q8JETY/g6ZLFb80BD0XTmqO6JC1DivXxcAwIVDu2FHXgWq602444td4v3d8cVOr+cxvX8X3D7FVlUZHqTCW9cMx9w3NkEQgIuGJXm8zei0aKw5WNxwjs5/l6xWAdd+uA1/H3N/jZjUKw6f/3MMnl6e7dRvNDpEhWvG9MCsgYkwmC2IDG5++E9EJCUdbNbU4LLwhvcEFquAepPFKeSLDfPcXsJ+m2oPQWJ4kAqVdUa37Xb8kpKIiMjBJxWFF1xwAe644w7x+uHDh9GzZ0/s3bsXY8eORUiILcAYPHgwsrOzG72vxYsXIzIyUvyve/fuvjjFDuOxn7JwWqvH3Dc24YSXvl0U2MprDE4DBH7bd9ppf2FVvVs11tGSGqw5WOJ0XWr9EfelkKsOFOOyd/9u1jn17OK8hPjvY7bH//toGQ4V2ZZg2pf8vHT5UFw2Ihlf3zbObTJhY8I1DAo7uopaozi45L5pvfDRjSMRpJLj4mFJ+O/cAZjaJx7x4Rr89x+N97ZUK53/6XruksFYfLGt8f2ve0+f9UCczUc9N7Zfd7jEbXnarFc3ilWMBZX1KK81QqOUY2rfBKfjVAo5xmfEYdbArvjv3P6Y1s95/7R+CXjuksEAbH8Xtj1yHt6/fiS6RjqW8w9MisSX/xyDz24ejYuHJ3s8x1snpYv9u67/cDu2H7ctKzaarfjfqkNuIeGQ7lG4ekwKXmyoerxwqGMgQLhGid2Pz8BDM/tgYFIkRrRgCTgRUWOa6kQUolaIPVF1erNTkHeJl9e/pGjb6+X24xUYv/hPp9frkxV1+MVLBTUAVNezopCIiMjO52uIjEYjXnjhBdx///3Izc1FWlqauE8mk0GhUKCyshLR0e4VIwCwYMECPPDAA+L16urqThcW5hTr8NX2k7jvvN6IDLFVb+w+WSm+AbI7UlyDHrHNr9iiwGAP3uwWrziEWoMZU/okICM+FHc2UoFk9/KaI3jz6uEA4DRhNTpEhQk947C8IXzMr6hv8r4EQcDfDeFOQrgGJToDFv1+CLUGC95YZxtiMrl3vFj1OLR7FIZ2j2r6B3UhVhTqbcuMZE01MKJ2yWC2wGwR8PTybIRqlJg3NAnvrD+G3/Y7Au8B3SJw3zTbMvVDT58vbv/oxlEwmK1OS2q9mdonHusOl+KuqRm4fFR3GMwWPPnrARRV63Giog5pcWf22pjvZbmuIAi46WP3Zf0VtUaMWfQnPrh+pBiMR4WooFJ4/x7uxglpuHFCGqr1JnywIReRIWrcND4VcrkMl41MbvTPYHzPuEbPXyaToX+3COzIq4TOYMbra3Nw73m98J8f9onN+6U+umEkYhuqgwFgcHIUFs7phx93F+DjG0c1+lhERGeqqYpCmUyGMI0S2noTdHqTuDQ4LS7U49RjABgkGdBWqNXjzXXOg9Z0ki8in790MB79KUsc1OapCpGIiKiz8nlQuHDhQoSFheG2227DwoULodFonPYHBQWhrq7Oa1Co0WjcbtPZTH95AwDg4815yF00G1tyy3HNB9swxCV84bef7Y9Ob8LqA8W4YEhXaJRNhx2eHG4ICgclRWJ/gW3YwWtrj+K1tc5veK8b2wNPzxuIw0U6zHxlg9M+6YTijyTLCNc+eA7WHS4Rg0LAtjzRW2NwwFZ5WNmw5CcxMkic5vrqnzniMS966MHWUvbeZ2ar0OywiNoPnd6Ex37Kwi97C516T0mXsdrdNjnd433IZLJm/95fuGwINh8rx5xBXQEAGqUC3SKDkVtWi+Jq/RkFhXvzq3Dhm5udthnNVqiVcpR6mLAsdctnmeLl5vbxiwhS4YEZfZy2teTPwBvpZM/Cqnrc9dUuFFc7zv+Juf0xPiMOvbuEeQzkb5mULg6SISLypSCVHHqTFZN6xTd5bHiQPSh0VBTalxd74voFzeps98nxAPDt7eMwOi0GGQlheG7lIWzNreB7aiIiIgmfBoV//PEH3nnnHWzduhUqlQoxMTHIyspyOkan00Gt9txbpDMzW6z4zw/7sGxXgdP2Yp0en/6dBwBuS948NdKntlVVZ8T87/fh0hHJmDkgETd+vAM7T1TieFktHprZp+k7cFFrMOOp5bbl+ef0iUf26WpYvCyjtFft9e4SBrnMuTF4ckP1qdUq4HCxLXhUK+WIDlXj4uHJKKrW47mVtqmzOn3jQeHeU1Xi5atHp2Dfqf1O+9PjQhEffvbhfqjkHHR6M4PCAHPv13uw9lBJk8f1SgjDhUM999dridgwDf4xpJvTtoQIDXLLapFVoMXY9Nhm31dFrRFXvLsFOS5L9gHgm8x8BKsUTh9Ov719HL7LzEefxHBkF1Zj2W7n121/D/wYmBSBPQ3/XhxrqCLUKOV4fG5/ZOZV4qJhSYgK4b/DRNT21j54DnafrML5AxObPNb+WqrTm6Ez2N7zNhYUAkC/rhEeB1FJjWzogzs8JRoXDk2yBYV8T01ERCTy2aeZ3NxcXHPNNXj77bfRv39/AMCoUaPwwQcfiMfk5eXBYDAgJoZ9jlwtWXHILSQEgCd+PoBNR8s83AKoru+YyyR+3lOAblHBGHWWAwlaW43BjLfXH8Mf2cX4I7sYX90yBjtPVAIAftlb2KygcE9+FX7aXQC1Uo7f9p1GQZVjKfCIHtF46fIhuPfrPU63Gdo9Cr0SwnDBEFsllUwmw2/3TEJBZT1251fizXXHoDdZkV1YjdmvbWw4xhZu2P3rnJ54b0MuqupM2H2yCtP6JUAhl3msLrJPR75hXA9cOiIZS3fkO4XWcWG+qQCWy23LjGoMZtQYzD4JH6ltHCqqxraGITrp8aEYnxGLSb3ikVtai/hwDayCgPyKOoxLj0W/rhGtdh4pMSHYmluBZ347iFOV9ag1mHHJiGTIAOSV16JnQjhG9IiGtt6EoyU6RIeokRYXik82H/cYEgK2vrBS14xJwei0GIxOs70+mS1WJEYG4a2/jonHhDXxQba1zZ/ZF+FBKrwtOacF5/fFNWN64JoxPfx4ZkTU2XWLCka3qOCmD4St6hqwBYU19opCTeMDld6/fgTu/GKXuCIDAAYnR2LfKdv1eUO7QS53vNexP0ZHfU9NRER0Jnzyaaa+vh4XXHAB5s2bhwsvvBA1NbYPXJMmTYJWq8Vnn32G66+/HkuWLMG0adOgULBSSGrDkVJ84GF5HuB92QTQMSsKb/houzh44/ji2e22T93uk5W45oNtqGsI0QDg6g+2iZdPVtQ1q8/eU78ewK6TVW7bJ/aMwzl9ElBrMGNSL9vla8akoFRnQPeYELfj+3WNQL+uETjZ0F9t6faTWLr9pLj/kuHJbn0DQ9VKVNWZnKau3j4lHQvO7+d0nH1KYHJ0CJQKOX6+awIA4M11R/HLnkIsunhgoz9jS4hBIXsFBYzVB4pw2+eO59CyO8f7rVrtX+f0xLeZtqnDnzRUYn/XMIXYbu6Qbvh1r/eG9l/dOga9EsJx8dubPfbwHJnq3DZDqZDjP7P64mRFnbikv7EK3bYQGazCZSOSnYLCOYO7NXILIqL2x149qNObxB6CTVUUJkeH4Ne7J2LMojViy4WHz++LGz/eAaPZipEuX0JHBNsnJXe899RERERnyidTj1etWoWDBw/i/fffR3h4uPhfQUEB3nvvPdxxxx3o0qULvv/+eyxZssQXD9lhCIKA6z/aLl6/97xe+PXfEzEsJcrt2HeuHYGLhychNdYWFGkb+qmc1tbj7qW7xWq2QCadznugsPGlI23JbLFCEAQIDc237/tmj1NI6Il0CrHdgUItPtx0HFarAIPZgr2ntB5uCfzrnAwAQKhGic//OQb/nJiGIJXCY0go1S0qyOP2WyaluW0rapjUKvXhxuNYe6gY6w6V4NwX/sKU59eJ1YNRIc7f4t81tSdW3T8ZPRPCGz2nlrB/AOAb9sBQWWt0CgmTooIRGdx4tUdrSo0LxSOz+zZ6TGMh4cb/TMX4jDjEh2tw5agUj8fMHOB5uZx9OAsA6E2Nvza0hThJRW6wSoG4MC41JqLAYn9PUGOQ9ihs3r8x0tUO/RIjsPq+yXj2ooG4YpTzgERHRSHfdxAREdn5pOxh3rx5YoDiKjU1FTk5OcjMzMT48eMRH9908+LORDpU4qJhSbh/uu3DprbO+Q3L+IxYzBqYiFkDE/HF1hNY+FOW+Kbm6eXZ+H1/EX7dW4i8JXPa7uS9MJqtuOr9rdh5ohIXD0vC85cNgULe8srA2z7LxKb/O9dpiUhjCqrqoZDJEBmswr1f78aUPvE+WWb3bWY+Hlm2H1ZBwBWjUhCkkuNEuWMy6oBuEegRG4Lf9xc53e7WzzKx+OJBGJ4SjcUrDuKvw44Q9Onl2fjXORli/8G7z+2JQUmRmDEgEQaz5YwHoUztm4AB3SKcQtbbp6SjTxf3MG/OoK74xSU0MVsF3PxJptuxAMSpxq3JHkZW1fENe3ulrTPh9bU5uHxUd9yzdLfTvkuGJ/m9Cvi2yRno1zUCggD0TAjDF1tPICZUjep6k9tAIJVCBrNVgP2fL2kQf9OEVBwtqUFGfCguGZGMJSsO4cbxqV6rBXsmhGF4ShR2naxqF8t7I4JU+PKWMdiQU4p5Q/3/eyEiail7KFitN4tTj5uqKLQbnhItvheKDlUjOlSNVA9DriKCHcubiYiIyKZN1kclJSUhKensm9d3RHdLPmi/fMVQ8fL903uL+7Y/cp7TUj57xU5FrW1J6KlK9+Vx/pRVqBWrG5ftLsAlI5IxoWdc07crcK6uK9TqcahIh/7dvPc0q6g14uU/juCP7GK3CrnV2cW4alQKdHozIkPOrMrp/Q25ePb3g+J16XLeqBAV5s/sgzFpMUiLC8N902oQH6bBvgItbmioEl2wbL/bfdrZe5qdPzARD0omn55pSGi/7W/3TELvR1fAaLEiPT7UbSmx3TMXDcSlI5IxpHsUQtUKPLfqMN7bkOv1vvu3Ym85O/vz3L7cuSP7dkc+BNjCZ39Yd7gEMSFqt2nqTXnsZ9tkY9d2CTIZMK1/Fx+e4ZmTTtP8zyxHheHswV3x9l/H0DcxAndMSYdMJsPB09V4enk27j63l9N9hKiVTq/Jr145rMnH/eCGUThcpMO4jOYPUmlNE3rGNeu1l4ioPQqTLD1uztRjqbvP6wkAuGF8aqPHicubDWZYrMIZfbFNRETU0fi3kVInVyIJthbOcQ5z5g7phtTYUGQkhLpVsNi//cw8UYlf9hYiNtQRIjanL15rqzM4L7szWazNup20ui0uTIOyGoMYhkrVGy148Ls9OFlRh6yCxpcnpz/yOwDbsu1ZzZiw53o+9pAwPT4UKrlcnCAMACvunYSukY6G3L0bqvZGt3AIy7/O6dmi45vj3etH4PmVh/F/53tfhhkRpMLk3o5AZYBLIOtamXimYWtLRIsVhR07KCzS6vGfH/YBAKb164JYHw2Eaa6jJTrc9PEOAGiyCrmgqh4vrj6Ma8f2wIBuEW5VqABwbNFsVNebEB3avpe39k2McAv8+nWNwFe3jvXJ/ceEqttNSEhEFOjs729LdQbUGloWFCaEB+HpeU33UJbeX81ZfLFMRETUkTAo9KOEiCBsWXAuMvMqMXeIe6P5QcmRHm8XonZUnC35/aBTRZC23uS3QQJ2rtVgNQ1v7rIKtDhUpMNFw5I8fmOrVthaZiZFBSM8SImyGgOu/XAbDj09CzUGMwqr6jE4OQqfb81zW+ZrNyYtBtuOV7ht/3lPQaNBocFswQcbbb0D5w7phlqjWVxWOTY9Bl/eMhYKuQw/7DyFB7/bi66RQUiM8NwPMFitwCc3jcI3O/IRFaLGttxyPDSzD/p1jUBEkBKxYRpsyinDtR9uw/iMWK+/57MxtU8CpvZJaNFtpBWDGqUcH980Ct9sz8eLfxzBvKFtMwjB/tzt6EuPM084nqNP/pqN165qulrNl77ali9eNpqtUCu9t6t9YdVh/Li7AMt2FXjsnfraVcOgkMvafUhIRESBJalhOnJhVT3kDV+CN7dHYXNplAoEqeTQm6z4esdJhKgVuG5cqk8fg4iIKNAwKPSzrpHBmDskuOkDJYanRIu9sAq1ehRqHaFZic7g96DQtRpMW2/Ca3/m4KU/jgAAFv9+EJ//cwy6Rgbho83HccnwZKTGhaKi4XaXjUzGK2tyxNt/ue0kFv9+EGargNmDEj2GhE/M7Y8JPePQu0s46o0WnKioxcVv/S0OHLEHT4IgwGQRoFbKYTBbsOi3g/h0ywmn+3qx4TztXr9quBhsXtKwVDdYrWi0cvOcPgk4p5GgbmKvOCy/eyK6RbXsd9+a0uPDxMsvXT4UCeFB+Pe5PTEuIxa9E303sKQx9h6FlR08KHxV8vz+ZW8h1Eo5VmYV4fpxPTB/Zp9WqwoWBAFPLz+IjzY7lg1r602ID/dc0VhvtODH3QXi9d0NE7rnDe2G68enIlStRJ82em4QEVHn0rXhPdJprV5su9PcisKWiAhSQW8yYPGKQwCA8T3jkCF5T0RERNTZMCgMQAq5DMv+NQHjF/+JQq1zX75SnUFcAtuaSqr1+PdXu3HRcFvvybyyWoxJj8E5vRPcQp5Hf8xCjKTaqLzWiNmvbRSvf70jHzsenYbKhmXGMaFqyGQQBww8vTxbPNYeEsaFaXDDuB7YdrwCr181zKmaKVitQN/ECCz713j8urcQb647hqp6E3aeqMAlb28BAEzuHY+qOiP2eZk6bPefWX3cQpSeCb558zgwyfeVhGdDIZdh9f2TsfZQCWYOsPWak8lkGNnCpdRnI7oh5NbWt/3SY0EQ8M2OfHy65QReunwI+vmgJ6MgCFh1oAgVtSbMG9YN32Wewo+7C5BTUuN03Pc7TwGw9a1ckVWEP+6fDKXi7IfSF1bVw2IVxCEdH2467hQSArY/a/tz3GIVYLJYEaRSQBAE3P7FTrf7BIBFFw/yOtSDiIjIF+zT2strjK1WUQg4r9QBgPNeXI9n5g3EtWP9P5iKiIjIH/hJL4A9OKMPHvxur9O2Ep3ey9G+NXrRnwCA7XmOJZTvbsjFYxf0FysHk6KCUVBlG7RSUWuEUi7DS1cMdZuUWqoz4K6vduFwka3/X3SIGu9fNxK3fOZ5+i4APHZBP1w4NAl3N3KOfRMjYDBZ8ea6Y8gp1uHGj3aI+zYcKXU7/l/nZIgDRgDgq1vHYHxG5xoE0LtLeJsEzd5EBTe/onBNdjFySmrEoRQt9cbaHPy+vwgJERqnidQAcP6rG7HmgcnYmFOGPflVeHROPySEe15q3phP/87Df3+1Bd2P/Og+2ObNq4fjrq92OW07XlaLw8U6DOhmC5LLagzYfLQMVkHApF7x+HlPIZ5eno07z8nAgG4RmDOoq8efv1RnwJzXNkJvsuKFy4bgl70FWHWg2O24aS9tcNv254NT8MKqw+Lfk0FJkTCYLThSXIMrR3VnSEhERK0uNtT2JZbRYsVpre39ZGtUFE7r18VtQNfCn7Jw6YhkBKnOfMAcERFRoOKnvQB2yYhkTOoVhz8PlWBFVhE2HClFSbWh1R/3iGSghytp9d8lw5Pw8eY86Bp6FJ7XLwH/GNINchnw76+cw8Lf9p0WL0eHqDGxVxzylszBd5n5mP/9PnSNDMJv90zCzhOV6N8tQuxb05SECNubTLNVEM/D1f3TeuP2KekIUilw1egUzH51Iyb3ju90IWF70Nypx4IgiEFyRa0Bj87p7/G4z7eewIurD2Nyr3jEhWkwNCUKFwzqinWHS/DCalugnX3a402dArSf9xTi+UsH4/xBXaE3WRAbqhbDubIaA1YfKEZZjQHdY4Kx/XgFxqTF4rf9p/FHtnswZ/fng1OQER8Gk2Uo7vtmj9O+Oa9twsSecSioqsfxslqPt3+7IdR+IvQAfr93ErILq1FRa8TsQV3x2ZY8cQkVALcwcsuCc/HQd3ux+Wi5x/t+9reDWHuoBAAwPiMWX94yBsXVBvy8pwDXs3cTERG1gWC1AsEqBepNFlgbVpm0RlD4wIzebkEhAJysqPPrl6dERET+IhME+wLP9qm6uhqRkZHQarWIiDj7pYAd1eLfD+LdDbm4ZkwKnr1oUKs8xtGSGvzz0x04UV4nbosNVaO81oi4MDXKapzDnX9OTMPwlGgxpPjl3xMwODkKAFBrMOO2zzM9BhW/3TNRrKYCgOJqPRRyGeLOcDLsXV/tEoPImyek4fG5/ZFbWoMDhdVIjw91eizA1pdNpZD5ZOkntcyhomrMemUjYkPV2PnYdK/HlVTrxapWABiYFIGbxqfhkhHJAABtnQl/HCzGQy4Vt03pERsCi1XAqcr6Jo+d2DMOT144ABe/9Te09Y1XQD5+QX881RCi33teL9wxJQPBavcqhVfX5ODlNUfctvvSsJQo/PivCcgrq8U5L/zV6LFyGZDz7GyPw4eIiIha24Qla8XVKQCQ8+z5ULXC+zOrVUBOSQ16dwlD2oLfAQDvXz8S0/t38fljERHR2akxmFFSrUduaS3G94zlaqcWaG6+xj/RDqJ/N9sv+cttJzGhZxwGJUViR14F9CYr5gzqisiGIREmixUfbjqOJSsOIVilwCNz+uG6Rnqw/LDzFD7begL9u4Zj6fZ8p313n9sTN01Iw/GyWozoEY1Hf9yPL7edFPenxoZgxoAuGJ8RC7lM5hTIhWqU+PKWsQCAfaeq8I83Nov7Ylymp3bxMl24uW6fnC4GhQ/N7A3ANrgj3Uujak8BDrWNqOCGqcf1JgiC4HVJ8Sd/5zldzyqoxoPf7cVFw5JQVK3H3Nc3obzWEVynx4eiW2QwNh0tE7epFXJkPjYNEUEqCIKAOqMFoRolLFYBB09X49Ef96NXl3BM6hWHe7/e43YOm46W4bwX1zf5M101ujtunpiGG8aniv3/vLl3Wi+U1ujxxVbH3yO1Qo7FFw9CRLAK//3lAMpqDPjq1jHQKBW4+v2tqNZ7rpQFgOV3T0R2YTX+b9k+vH3NCMSHq9E30fZakRoXiuynZuLNdUcxJi0Wk3vHw2SxYtziP8XQ/9y+XRgSEhGR38SEqsWgMDxI2SohIQDI5TJxONe5fROw9lAJKmpbf5UOERG13KVv/41DDW3LBiVF4ue7JkDOzyw+xaCwgzi3b4K4PONfXzovM3zkx/0YmBSB68el4uPNeTh4uhoAUG+y4NU1R3Cqog4/7CpAWY0Bd0zJwKyBidiaW47c0hp8m2kbsrA3v0q8v3CNEr/cPRFpcaEAHMHek/8YgNgwDbILteiTGI4rRqVApZDjq1vHNnrug5OjMDo1Rux3GO3jqc2Dk6Ow9Nax6BKh4bcN7Zx96rHFKqBabxanHEpV1hrFXpLSoTcAUK034dMteU4h4S0T07DwAtvS5P/+ckAMGefP7IOIhqboMpkMoRrbc0Mhl2FgUiR+/vdE8T4uHJqEshoD1h4qQWFVPdRKOT7adNypivb+ab3x5rqjMFqsAIBZAxKxYHZfpDQMElHIZVDImw6hn75wIO6YkoFukcGQy2WwWgXxH77JveNQZ7CIw3v2/Xcm6oxmfPr3CfRKCENsmBqP/piFPonhWHLJIGiUCgxMisTlo7p7fKwQtRLzZ/YVr6sUcpzbN0H8e9+7C6c+EhGR/0i/PHb9Irm1H/OjTXm4YlRKmzwmERE1T1mNQQwJAWB/gRa5ZTXomcBWEb7E1KSDCA9SYdV9kzH5+XUe92cVVOM/3+9z215WY8S7G3LF6++sP4Z31h9zO05qzxMzPFYZKRVyPDC9dwvP3CYi2PFUbI3G0eMyYn1+n+R7QSpHPyJtncljUPi/lY7eexv/MxUv/XEEy3YVAADWHynFu+ttz+eXLh+CtLhQp+nF//3HAEztm4DoEJW4DL654sI0uHykI3C7cXwqXl2Tg01Hy/DudSOQHB2Cf05Kg0ImQ37lmfc1kslkSI4OEa9Lvx3TKBXQKJ3/foSolbjznAzx+u/3Tjqjx7W7aUIalu87DbNVwAWDu53VfREREZ2NWD8GhYeLdfj7WBl7VhMRtSP2PuqArajhSHENMvMqGRT6GJuwdSApsSH49d8TMTY9Bs9fOhhrHpiM8wcmuh333R3jkLdkDp6ZN1Dclt5QHejJf+f2x/r552Bsegw+vGFkqyxFPJOptdQxRTdUFZZ7WPLz0abj+HqHbQn8nEFdkRwdgpcuHypWt0qXCI9Jj8WwlGi34HlK7/gWh4SehKiVWDC7H367Z5IY7IVplAhWKwK6+Xm/rhH466FzsO6hc8SWBkRERP7gVFHo4xUn3nSPdgzMO1LkfYBfc6zJLsYba3NgNFvP9rSIiDo9QRDwwUZbUcgN43rgwqFJAICHl+3HrFc2oN5o8efpdSgMCjuYQcmR+Pq2cbhsZHf0TAjH29eOwLJ/jRe/kV153ySMSo0BAFwzJgV3TMnA6LQYfHnrGCy+2DEE5ZHZfTGkexRmDuiCK0enoEdsKL6+bRzO69c6TZ3ZUoDs4sJtQ2tOVtTh5z0FKKnWA7D113zpD8egj4UX9BMvu07BntYvodmTscldQkQQ//yIiMjvoiVBYXQbVRRKlxsX686sT6HeZMHyfYW45bNMvLD6CH7aU+Cr0yOiAGWyWPHO+mNYfaDI36cSsE6U1+FIcQ0A4M5zeuKaMSlijnCoSIef+VrrM1x63AkMT4n2OEFWJpPh4fMd/cmuGp2CiT3jYLYKSIsLxW2TM9xu01r+dU5PrDpQjIuHJ7XZY1L7VKS1BYP26sALBnfFG1cPx+EiHWoMtsEdWxech8RIx5CbB2b0dhpU8va1I9ruhImIiKhVSJcex7ZRUKhWyvHYBf3x9PJsnCyvO6P7uPCNzThc7KhG/GrbSVw2Ihl78quQEBGEg4XVGJsRizCNEoIgQBCA51YdRrXehLvP7YmukfyyjqijMJgtqKg1Yk12sThQNOvJmRwYeAbssxYGJ0eKnwX/fvg8TH95PXR6Mx5eth8jekSjZ0IYZDIZBEFAqc6AhEaGo36wMRff7zyFhXP6Y2IvtpqwY1BITrrHhDR9UCsY0j0Kux6bjigPPemoc1u+7zTeuBr4abftG6LxGbFOISFgC8OlWmsqIhEREbWdGD9UFAJAaqzt/fDxstoW37bOaHYKCQFgT34V0hb87nbsDeN64NMtJ5y2fbXtJADbB+Glt44Vh60RUeARBAGXvr0F+wu06B5j+wKg3mTB8bJa9Ezg0MCWsr+29pG0eUqMDMKKeydh4v9ssxqmv7wBV41OwY3jU/Hgd3uQVVCNZy8aiGvG9HC7P6tVwP9WHoLJIuDaD7fh+OLZbInWgJ+mqd2ICVVzrDnh05tHO10PVdt6DOaU2MrMp/f3vPz97nN7Ov2fiIiIAltsmEa83FY9CgGIvYZzSnROPa8Kq+pRZzR7vZ0gCNiWWyFe37rgvEYfxzUklNp3SosnfjnQ3FM+K38eLMa23PI2eSyijsRqFQAA+RV14mWpEp0B+wu0DcfUi9vP5EuI9iqvrBZvrjuKeqMFB09XY/arG/HX4RLo9Ca8sTYH32XmQxDc/2w8sVVYux9rNFvx8ebjeGVNDgCgT6JzP/jk6BDcOilNvL50+0nMfGUDsgpsFYiP/pgFbb3J7X6/3pEPk8XxeO+sz3U7prPiV1RE1K706xqBvCVzkF9Rh0nPrUOt0YLUh39DYkPJeGqs58E795zXC+f0icegpKg2PFsiIiJqLQnhjqCwLSsKk6OD0SVCg+JqA5asOIjcslpM69cFT/xyAGPSYvDN7ePcbrPzRCUueftv8fo1Y1KQGBmEXY9NxyVv/+0UDDx3yWD854d9Tre/aFgS1hwshk7vCCK/33kKFw1LwoSerbccbkdeBf75aSYAIHfRbH5pT9RMn2w+jmd+O4iwICWq6kyY1CsOl4/sjun9u4jDFJ/97aDH2976WWa7+/tmslhx3YfbcKK8Dp/dPBq9GhnOWFytx/GyWtQazOLrx/OrDov7b/x4B0anxWD7cdsXJ0lRwRjfjNexe77eg6255Xju0sGY2idB3P74z1niQEsAHgcuXju2Bz7fegJ6k+fhUe9vyMVDM/sAAE6W1+G1tTn4fucpp2P+t/IQ4sLUmNavS5v+m9MeMSgkonYpKSoYo1KjsSOvEgBQ1DDUJE5SXSClUsgxokdMm50fERERta6ECMe/+W3ZVUQmk2Fkjxj8tv+0WPW3McfWC3nb8Qq3461WAY/+uN9pm70qMSZUjXUPnQOrVcCBwmqkxIQgMkSFiGAl3lh3FDGhGvx7ak+MTouBtt6EylojagxmPPLjfuw7pcXyfacxPiPWp8vh8spqcc/XuzE6NQYfbDoubs8tq0HPBO/hABE5vL72KMxWAVV1tkq1jTll2JhThoVz+uGWSek4UKjFL3sLvd7+860ncMP4VI/7dHoTnvo1G0NTojwumfUVbb0JGqUcNQYzth+vwNaGiujpL2/A5/8cjUm94j3e7r6v92BLE1XI2yWvlXnldRjfxKIvvcmCXxv+vO78Yidev2o4DGYLlu89jZWSATAKuUwczirVIzYUOxdOhwDgmg+2YW9+FZ67dDAsVgELlu3HG+uOQqWQ419TM/D86sPiYwHAkosH4eFlttfw+d/vw//N6os7z2m7eQ3tEYNCImqX5HIZvrltHK79cBv+Pub4h6hLpOegkIiIiDoWjVIhXu4Z37YB1sjUaPy2/7THfTUGM8I0StQZzQhRK7HnVBUOFTn3JezX1bniRS6XYVBypHh91sCumDWwq9MxkcEqRDb0675zSgbu/HIXlm4/ia255RibHoul221DUf53yeCzqkT6768HsO+UFvtOaZ22T3tpA/KWzDnj+21KfkUdQjVKp96TRIFEEATkltViw5FSlNcaPR7z3KrDyCuvFb8sAICFc/rhGZfqwid+OYBZAxPRpWHV1LeZ+SjW6nHX1J547c8cfLfzFL7beQoz+iciPvzsP/8IgoAtx8rx0eY8HCutwZ1TMvDU8mxxWKSr+7/Zi78fPhdqpeNbGotVgFUQvIaEaqUcRrN7Rd8jP+7HxcOTxEpLT/7ILhYv601W3PpZptsxj87uh0tGJHvtR2/v6frzXROctu8+WYlvM0/h5TVH8PKaI077bp+cjitHp2BjTpn4mj/AQ8ViZyMTmrtg3E+qq6sRGRkJrVaLiAj+wog6G6tVQPojjgbgrfkGloiIiNqXoyU1KK8xYEx6bJs+7mltPcYtXgsAeOWKoRjRIxqTnrM1y79xfCpKdQb8tv80glUK1JtsfQzP7ZuA/8zqg+zCalw0LOmsqgD1JgtmvbIBeR4mL6fHheKxuf3x99EyfPr3CXx56xiPFTZSJosVW3PLUVxtwEPf7fV6XPZTM7FsVwGWbj+J964fiaQo30xgrqw1YvyStVAqZNj4n6mIasOek0QtJQgCPtx0HLFhalw0LFnc/u2OfLe2AS9cNgRj0mLw4abj+OTvPLf7Gpcei49uHIUZr6xHRJAKBwqrxX0f3jAS5/XrgiKtHmMX/+n1fN67bgRmDEgEYHttsFiFFg86+mTzcfz31+wW3Qaw9Ycfmx6LH3aeQl55LeYM6orvXJbsHn5mFnKKaxCkUiAjPhTXfrgNm4+6h4kr75uEvonOmc6e/Cp8syMfS7efbPQ8tj96HhLCvU8vbkxBVT2u/WCbW2/Ixy/oj+vH9YBSIYfVKqCgqh7J0cEdeqBJc/M1BoVE1O6tOlCEO77YiQ+ut/1jSkRERNTaLFYB+RV1SI2z9Uf+5yc78OehEq/Hn9MnHp/cNNrr/pYymq04WlKDeW9uhtHiue8WAEQEKbHvvzMB2EKEIJUCdUYzzFYBEUEqHC7SYeYrG7ze/tUrh+Ler/e4bZ87pBtev2qYx9scK63BkhWHsOtEJe6b1gvXjUtt9GfJzKvApe9sAQC8e90IzGwIPYjao50nKnDJ27bn64EnZ4qhXOrDvzkd9861IzBroO25bLUKmP3aRrfq4tsmp+OR2f2gN1kgl8lw99JdWHXAVj234Py+uG1yOgb9d7XXyj67J+b2x5FiHZZuz0eIWoE1D0xBNw9BvtUq4FhpDXomhImBl8UqYPjTf3gc6OHqyX8MQHJ0sNh70JsesSG4eUIahqVEYXBylNM+21LmcgzsFokZr2wQl2cDwJ7Hp4tfFBRX6zFmkXNAesnwZPywyzmInD0oEW9dM6LJc2/K30fL8MiP+5FXXoeJPePwxS1jzvo+A01z8zUuPSaidm/mgEQcX8xKQiIiImo7CrlMDAkB4PYpGWJQqFHKYXBZYtcrIcynj69WytG/WwQ+unEUNh8rw3l9E3D/t3ucpqcCQLXejJs/2YEuERos3Z4PtVIOTcPSvE//ORo/uFT/AMBHN45EeY0Rc4d0Q5BK4TEo3H68HGaLFaU1BvyRXYwrR6WIyxBv+ngHTlbYqh0f+/kARqXFuFUKSd355S7x8kkPVZJE/nS0pAZ78qvwjyHdYDBb8LlkIvmAJ1bh4uFJOF2ld7rNq1cOFUNCwNZe4ItbxuDNdUex/nApchuq18ak2ap97ctuF100CFtzK6CtN+G0Vo91h0vcQsLoEBWev3QI4sM1uOfr3ThRXocnJdWAdUYL/vXlLnxz+1ioFXKnCrjvd57Cf37Yh5snpOG+6b2wM68SGpUc2noTwjRKZC6chl0nK3H1+9sQqlbg8bn9MaV3Am79LBPaehPO7ZuA7jEhWH73RLyz/hh+338aHgY6466pPXH5yO4e/zzDNEqc29dW3LH7sel44Nu9+HF3AQDggtc34bd7JiEyWOVWRfjB9SMxsVcc4sLVGNkjBi+uPoxQjRKPzO7n8XFaanzPOPw1fypyS2sQ66XvPdmwopCIiIiIiKgZdp+sREpMCEI1SshkwKHTOqgUcvy8twB3n9sLYS1cDthSgiDAYLbiRHkdukUF4dnfDjpNA21Mr4QwTO2bgEtHJDv1TwPcpzbbXT4yGZl5lcgtq8U/J6bhsQv6e1x+ecnwZLx4+RC322/KKcO7G46Jw2AA20ToZy8a1KxzJmoL/3hjk1vPzsZkLpzmdcAiAFTrTbjgtU1Ijg7GpzePduup9/mWPDz28wGnbb0SwvDHA1Pc7ktbb8Ki3w7im0zvf89n9O+Cp+cNRJeIIFz81mbsOlnl8bhz+ybgoxtHNfKTuRMEAc/8dhArs4owb1g3HCupxUXDk1pcFfz+hlw8+7ujT+OrVw7Fj7sL8NfhUvSIDcGdUzJwxajuHXrZb3vApcdEREREREQdmNUq4M4vd4pLGe2m9onHsdJaseoPAPY+PgORISqP91OtN2Hwf1c3+XiJEUEoqnZUViWEa1CiM2BQUiR+vXuiuL3eaMHKA6fxfz/sdxtuMKlXHD7/Z+db8kftk7QfqdTApAhM7BmPd9Yfc9t3fPHsJgMte8zi6bjDRTpc8PpGmCyOKOayEcl4/jL3sN3u4OlqRASrkBQVjGeWZztNLLeb0b8LVmcXe7i1zaOz++HWyemNnndr+nzrCTz2U5bb9k9vHo0pvT1PWCbf4tJjIiIiIiKiDkwul+Hd60bip90FiApRYWLPOMhkMijkMlitAhYs249vMvMxLj3Wa0gIABFB3vdJSUPCj28chehQNea9uRn7C7S48M3NSI4KRkyoGlEhKry+9qjTbe0h47bcCgiCwMqhTkBbb8Lrf+Zg3rAkDEyKbPoGbajWYEat0ewUEiZFBWNo9ygkRgbhilHd0btLOOYN64byGiNiw9R48Nu9uG1yerOeu40d0ycxHLsfn4FDp6vx054ClFQbcOc5GY3en3SS+qNz+mFEj2gUV+uRfboa32ba2gtIQ8KrRqegxmDGiJQovPnXMdQZzE5Lpf3hurE9kBZrG3Yi1S3yzIaUUOthRSEREREREVEHlV9Rh8gQVZNh4Pojpfh5TwGW7SrApSOSMbl3PH7YeQpbjpVjVFq02xTTvCVzoDdZcPFbfyP7dLWXewV6JoThf5cMQmpsKEY8swaArU/b1WNSzv6Ho3bt8Z+z8FlDv7+8JW3bb7xEp8c7f+ViYFIEDhXpMC49FlP7JgAAXv8zBy/+ccTp+K9uHYPxGXFteo6+8ufBYqfhI/+7ZBCuGOX4+yUIAowWKzRKhT9Oz410ArNCLsOBJ2eKPRypdXHpMREREREREbVIcbUecWEaKOS2iii9yQKNUo6KWqMY9C2+eBCuGm0LIgRBwB/Zxbjt851O9xMepMSiiwbh/IGJUDb0aBv61GpxAupdUzPw4PQ+kMtZWRio9CYL6owWxISqnbYfKqrGBxuP43vJIJ3WDgoLqurx8A/7oDdZcPnI7pj/vXMfTZkMuHtqT1w8PBkzXt7gNEn8k5tG4Zw+Ca16fm2hWm/C6So9+iSGN32wH+3Jr8K8NzcDAPomhmPlfZP9fEadB4NCIiIiIiIi8qkirR4J4Rq3gG9rbjkqao3IK69Fdb0Z143rgaSoYKdjjhTrMOPlDeL1cemxmDO4K64c1V0MEwFbADX/+334dW8hAOC1q4bhH0O6teJPRY3ZcqwcG3NKcc95vcTKr8NFOlz53hZU1pnw/KWDMW9YEn7cVYDJveNx+xc7sTe/yuk+Dj8zq1Uq2swWK77ekY+FHnrfNWXO4K6YOSCRz602ZrJY0f/xlTBZBCw4vy9un9L4smvyHQaFRERERERE1K7868ud+H1/kdO2tLhQLLtzPLbnVUCjlOPGj3e43W7Ho9OQVahFZLAKw1Oi2+p0CUDqw78BAOLDNXjqHwOw+VgZvth60umYod2jsMclHJRaff9kt2nbJosVVkGAIOCMl56+ue4onl912G17UlQw7p3WC7UGM6b164JVB4qweMUhWKy2+IPhs3/9ebAYOSU1uHVSuli9TK2PQSERERERERG1K3VGM5757SC+3ZEPs/XMP4oeWzQbCrkMJosVKkk14tmoqDUiq0CL0WkxZxxc1RnN2HmiEpuOlqF3QjjCg5Q4WVGHiGAVkqODMax7NAxmCwxmK7pEtK8hDkdLdFh3qBQ3TUiFUiGH0WzFrpOVuPK9rWd0f7MGJOJkRR2yT1fjnWuHY9bArjCYLbjl00xszCkTj4sOUeF/lwxGWlwoenVp/rJZQRAwZtGfKNEZAADDUqJww7hU9IgNwTAPYXJlrRF3L92NmFA1XrhsCNRK3zxviAIFg0IiIiIiIiJqlwRBQJ3RgiPFOtz2+U6UNoQ9dl0jg/D7PZPwxdYTboMnXN1zbk9cNSYFXcKDUFFnRFyYpkXnUqozYOoLf6HGYBa39U0Mx6RecUiODsFlI5NRozdDb7IiJTZEPKaqzoickhqsP1yKId2jcLKiDs+tPASD2erpYdxM798FD87ojb6Jzf+cW2MwI1St8DhVN7+iDiaLFenxYeI2q1Xw2gfyWGkNKmuNGJ4SjePltTjvxfUAgFeuGIo5g7tiwBOrYGzkZ3n+0sFY+FOW+POePzARfRMj0DUyCJN6x6FrZDDu/2YPftxdgNmDEvHPiWm444tdbr9rqXP7JuDykcmYOSCx0cnBBrMFl7+7FXvzqyCXAXuemNHs6d1EnRWDQiIiIiIiImr36oxmfLH1BHolhGNAtwhoVApEBjtCn10nK/HbvtMIUSvw+tqjTd5fZLAKI3tE4+Hz+6JEZ4BCLsOpynqkxoZAANAnMRxfbj2Jg6erMSgpEs/+frDZ5zqkexTqjWYcKa45kx/VI6VcBrNVQEK4BosvHoSJvWzTdzVKBQqr6vH+xlzEhqoRG6bBgmX7kR4XipkDE3HB4K7463ApCqrqoTdZsGxXAYJVCmz8v6mwCgIe/+kANh0tw4wBXfDUhQPx58FiPPjtXsSFadAtKgi7TlYBAO48JwOZeRXYkVfp9Ry/vm0sRqfGIKtQC0EAduRV4Ibxqdh3qgrvrs/FrIGJuHh4stvt3ll/DEtWHPJ6v+cPTMSKrCK37b27hMFsEbBgdj9M798FgGOwjkwmww87T+HB7/YC4EAMouZiUEhEREREREQdit5kwfbjFTitrUdBlR5ZBVqsPVTik/u+ZWIaLhmRjI82HUe9yYLDRTrklLQsEByXHovLRiYjSKXA8bJaxIWpcfnI7gCAv46U4rGfsnCqst4n59tWNj98rttgmuaqNZjx4Ld7sfKALQwMD1LihzvHo6zGgOEp0QhSKaCtM+FkRR3e2XAMv+077XYfcwZ3hUYhx/L9p3HB4K64anQKLntni7j/vetGYMaAxDP74Yg6EQaFRERERERE1OEVVtVjzcFi1Bos6JkQhmOlNfjfykNo7ifdPl3C8a+pGbhwaJLbPr3JgjUHi/FHdjEig1WoN1qg05tx/qBEpMaGQltvwsSeccgrr0VEsKrZy54FQcCWY+UwWqw4UFiNDUdKse14RUt+bAC2asTz+iVg18kqj0t648I00OlNTsuhbxyfiso6Iyb3iseb644it6wWgK2ycGdeJfaeqkKYRolhKdF465rhPunld7REh23HKzB3SLcmlwjvPlmJDzcdx3IPoaGrZf8az+E2RM3EoJCIiIiIiIg6JaPZioOnq9EjNgQ78ioxOi0G+09p8dfhEnSLCkZUiAq9u4SjW1QwYkLV/j5dALbwcEdeJTYfLcOag8U4UFgNAPjujnHokxiOzTll6N8tAhFBKgSrFdh+vAK9u4QjMTIIgiDAYLbi9s93YtvxcpzXtwv+OSkNw1OicbSkBs/8lo1RqTG4aUIqQtRK8TELqupx3QfbMDg5EksuGXzGQ1xaw1t/HcULqw7D08ybMI0SC2b3xTVjerT9iREFKAaFRERERERERAFKEIRGB3p4U2+0IFjdfgK/s/Xi6sN4fe1RDEyKQIhaibum9sTY9BholB3nZyRqC83N15Re9xARERERERGRX5xJSAigQ4WEAHDPeb1wbt8EDEmO8jrBmYh8h0EhEREREREREbVLKoUcw9iHkKjNnH1XUiIiIiIiIiIiIgp4DAqJiIiIiIiIiIiIQSERERERERERERExKCQiIiIiIiIiIiIwKCQiIiIiIiIiIiIwKCQiIiIiIiIiIiIwKCQiIiIiIiIiIiIwKCQiIiIiIiIiIiIwKCQiIiIiIiIiIiIwKCQiIiIiIiIiIiIASn+fQFMEQQAAVFdX+/lMiIiIiIiIiIiIAo89V7PnbN60+6BQp9MBALp37+7nMyEiIiIiIiIiIgpcOp0OkZGRXvfLhKaiRD+zWq0oLCxEeHg4ZDKZv0/H56qrq9G9e3fk5+cjIiLC36dDHRyfb9TW+JyjtsTnG7U1PueoLfH5Rm2Jzzdqa3zOtT5BEKDT6dCtWzfI5d47Ebb7ikK5XI7k5GR/n0ari4iI4F8GajN8vlFb43OO2hKfb9TW+JyjtsTnG7UlPt+orfE517oaqyS04zATIiIiIiIiIiIiYlBIREREREREREREDAr9TqPR4IknnoBGo/H3qVAnwOcbtTU+56gt8flGbY3POWpLfL5RW+Lzjdoan3PtR7sfZkJEREREREREREStjxWFRERERERERERExKCQiIiIiIiIiIiIGBQSERERERERERERGBQSERERERERdQrl5eX4+++/UVZW5u9ToU6Az7fAxKDQj7KysjBq1ChER0dj/vz54FwZOlt33303ZP/f3v2ENP3HcRx/raZlNDDD6rJisoMmSRFS1LFDHdYlug0CozKKCoKoQ6dOHTx4CMxOE6Kky5AIwlCILkKGLWLSoZTBCPrDwpWgA9+/w4/f0OV35td9+frt93zADt/t8jk8eTE+DBYKlV/xeFxS9dboEKv1/ft3xWIxTU9Pl99z2xj9YSXL9ea0dRK9YW2GhobU0tKicDisQ4cOaXJyUhIbB2849cbGwSuDg4OKx+O6fPmydu/ercHBQUlsHLzh1Bsbt/5xUeiTubk5nTx5UgcPHtT4+Liy2axSqZTfx0LAvXnzRs+ePVOhUFChUNDExETV1ugQq/Xt2zclEokllzZuG6M/rGS53qTlt06iN6zNx48f1dXVpbt37yqfz2vPnj06d+4cGwdPOPUmsXHwxo8fP3TlyhW9evVKExMT6u/v182bN9k4eMKpN4mNCwSDL9LptG3bts1+/fplZmZv3761o0eP+nwqBFmpVLJIJGLFYnHJ+9Vao0Os1rFjx6y3t9ck2dTUlJm5b4z+sJLlenPaOjN6w9o8ffrU+vr6ys+jo6NWX1/PxsETTr2xcfBKLpezhw8flp8zmYxFIhE2Dp5w6o2NCwZ+UeiTTCajw4cPa8uWLZKkjo4OZbNZn0+FIHv37p3MTPv371dDQ4NOnDihXC5XtTU6xGo9ePBA165dW/Ke28boDytZrjenrZPoDWuTSCR08eLF8vOHDx8Uj8fZOHjCqTc2Dl6JRqNKJpOSpFKppJ6eHp06dYqNgyecemPjgoGLQp/MzMwoFouVn0OhkDZu3KhCoeDjqRBkk5OTam9v1+PHj5XNZlVXV6fu7u6qrdEhVqulpeW399w2Rn9YyXK9OW2d5L5FoNL8/Lx6enp06dIlNg6eW9wbGwevZTIZ7dy5U8PDw+rt7WXj4KnK3ti4YOCi0CfhcFibNm1a8t7mzZs1Ozvr04kQdMlkUmNjY+rs7FQsFtO9e/c0PDyshYUFx9boELVQrSO3nwFOnLZuZmaG3lAzt2/f1tatW3XhwgU2Dp5b3BsbB691dHRoZGRE7e3t6urqYuPgqcre2Lhg4KLQJ01NTfr69euS94rFourr6306Ef42jY2NWlhY0K5duxxbo0PUQrWO3H4G/Kn/tu7z58/0hpp48eKF7t+/r0ePHqmuro6Ng6cqe6vExqHWQqGQDhw4oFQqpaGhITYOnqrsrfIXgGzc+sRFoU86Ozs1NjZWfp6entbc3Jyampp8PBWC7Pr163ry5En5+fXr19qwYYP27dvn2BodohaqdeT2M8CJ09ZFo1F6w5p9+vRJyWRSfX192rt3ryQ2Dt5Zrjc2Dl4ZHR3VjRs3ys/hcFiS1Nraysah5px6u3PnDhsXBP7+l8r/V6lUsubmZhsYGDAzs+7ubkskEj6fCkE2MDBg8XjcXr58aSMjI9ba2mpnz56t2hodwi1V/Autm8boD39qcW9OW2dGb1ib2dlZa2trs/Pnz1uxWCy/5ufn2TjUnFNvqVSKjYMn8vm8RSIR6+/vt1wuZ2fOnLHjx4/zPQ6ecOqN73HBwEWhj9LptDU0NNiOHTts+/bt9v79e7+PhIC7deuWNTY2WjQatatXr9rPnz/NrHprdAg3Fl/cmLlvjP7wJyp7c9o6M3qDe+l02iT99pqammLjUHPVemPj4JXnz59bW1ubRSIRO336tH358sXM+B4Hbzj1xsatfyEzMz9+yYh/5fN5jY+P68iRI2pubvb7OPiLVWuNDlELbhujP9QavcELbBzWC3qDF9g4rBf05j8uCgEAAAAAAADwZyYAAAAAAAAAuCgEAAAAAAAAIC4KAQAAAAAAAIiLQgAAAAAAAADiohAAAAAAAACAuCgEAAAAAAAAIC4KAQAAAAAAAIiLQgAAAAAAAADiohAAAAAAAACApH8AFUDcREzS7t4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x287b618b860>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./000025.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3575 entries, 0 to 3574\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3575 non-null   datetime64[ns]\n",
      " 1   open        3575 non-null   float64       \n",
      " 2   high        3575 non-null   float64       \n",
      " 3   low         3575 non-null   float64       \n",
      " 4   close       3575 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 139.8 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3570</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>17.92</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.61</td>\n",
       "      <td>17.87</td>\n",
       "      <td>18.034</td>\n",
       "      <td>18.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3571</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>17.86</td>\n",
       "      <td>17.96</td>\n",
       "      <td>17.60</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.918</td>\n",
       "      <td>18.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3572</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.68</td>\n",
       "      <td>16.99</td>\n",
       "      <td>17.06</td>\n",
       "      <td>17.676</td>\n",
       "      <td>18.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3573</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>17.15</td>\n",
       "      <td>17.29</td>\n",
       "      <td>16.70</td>\n",
       "      <td>17.23</td>\n",
       "      <td>17.576</td>\n",
       "      <td>17.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3574</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>17.24</td>\n",
       "      <td>17.28</td>\n",
       "      <td>16.75</td>\n",
       "      <td>16.92</td>\n",
       "      <td>17.350</td>\n",
       "      <td>17.769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10\n",
       "3570 2024-12-19  17.92  18.05  17.61  17.87  18.034  18.258\n",
       "3571 2024-12-20  17.86  17.96  17.60  17.67  17.918  18.161\n",
       "3572 2024-12-23  17.67  17.68  16.99  17.06  17.676  18.046\n",
       "3573 2024-12-24  17.15  17.29  16.70  17.23  17.576  17.931\n",
       "3574 2024-12-25  17.24  17.28  16.75  16.92  17.350  17.769"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  11.39  11.43  11.11  11.19\n",
      "1    2010-01-05  11.19  11.29  10.79  11.21\n",
      "2    2010-01-06  11.20  11.50  11.10  11.32\n",
      "3    2010-01-07  11.23  11.39  10.83  11.05\n",
      "4    2010-01-08  10.96  11.14  10.92  11.14\n",
      "...         ...    ...    ...    ...    ...\n",
      "3600 2024-12-19  17.92  18.05  17.61  17.87\n",
      "3601 2024-12-20  17.86  17.96  17.60  17.67\n",
      "3602 2024-12-23  17.67  17.68  16.99  17.06\n",
      "3603 2024-12-24  17.15  17.29  16.70  17.23\n",
      "3604 2024-12-25  17.24  17.28  16.75  16.92\n",
      "\n",
      "[3605 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3605 entries, 0 to 3604\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3605 non-null   datetime64[ns]\n",
      " 1   open        3605 non-null   float64       \n",
      " 2   high        3605 non-null   float64       \n",
      " 3   low         3605 non-null   float64       \n",
      " 4   close       3605 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 140.9 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.11</td>\n",
       "      <td>11.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.29</td>\n",
       "      <td>10.79</td>\n",
       "      <td>11.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>11.20</td>\n",
       "      <td>11.50</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>11.23</td>\n",
       "      <td>11.39</td>\n",
       "      <td>10.83</td>\n",
       "      <td>11.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>10.96</td>\n",
       "      <td>11.14</td>\n",
       "      <td>10.92</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3600</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>17.92</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.61</td>\n",
       "      <td>17.87</td>\n",
       "      <td>18.034</td>\n",
       "      <td>18.258</td>\n",
       "      <td>18.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3601</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>17.86</td>\n",
       "      <td>17.96</td>\n",
       "      <td>17.60</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.918</td>\n",
       "      <td>18.161</td>\n",
       "      <td>18.271000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3602</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.68</td>\n",
       "      <td>16.99</td>\n",
       "      <td>17.06</td>\n",
       "      <td>17.676</td>\n",
       "      <td>18.046</td>\n",
       "      <td>18.220333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>17.15</td>\n",
       "      <td>17.29</td>\n",
       "      <td>16.70</td>\n",
       "      <td>17.23</td>\n",
       "      <td>17.576</td>\n",
       "      <td>17.931</td>\n",
       "      <td>18.155333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>17.24</td>\n",
       "      <td>17.28</td>\n",
       "      <td>16.75</td>\n",
       "      <td>16.92</td>\n",
       "      <td>17.350</td>\n",
       "      <td>17.769</td>\n",
       "      <td>18.091333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3605 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "0    2010-01-04  11.39  11.43  11.11  11.19     NaN     NaN        NaN\n",
       "1    2010-01-05  11.19  11.29  10.79  11.21     NaN     NaN        NaN\n",
       "2    2010-01-06  11.20  11.50  11.10  11.32     NaN     NaN        NaN\n",
       "3    2010-01-07  11.23  11.39  10.83  11.05     NaN     NaN        NaN\n",
       "4    2010-01-08  10.96  11.14  10.92  11.14  11.182     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "3600 2024-12-19  17.92  18.05  17.61  17.87  18.034  18.258  18.291667\n",
       "3601 2024-12-20  17.86  17.96  17.60  17.67  17.918  18.161  18.271000\n",
       "3602 2024-12-23  17.67  17.68  16.99  17.06  17.676  18.046  18.220333\n",
       "3603 2024-12-24  17.15  17.29  16.70  17.23  17.576  17.931  18.155333\n",
       "3604 2024-12-25  17.24  17.28  16.75  16.92  17.350  17.769  18.091333\n",
       "\n",
       "[3605 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGQCAYAAACTXgvJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABym0lEQVR4nO3dd3hUVf4G8PfeaekJKZBCC72DdHVVmh0XsKOIHXXRdVd0Xf3tWnZVZEVlsXcFLLAg2BBFmiIoIEVaKCEQ0nufTLv398ckUzIzyUwyycydvJ/nQWdum3MyyZ3vnPM95wiyLMsgIiIiCgAx0AUgIiKizouBCBEREQUMAxEiIiIKGAYiREREFDAMRIiIiChgGIgQERFRwDAQISIiooBhIEJEREQBw0CEiIiIAoaBCBEREQWMOtAF8FZ5eTnMZrNfr5mUlITi4mK/XjOYsb6hr7PVmfUNbZ2tvkBo1VmtVqNLly4tH9cBZfELs9kMk8nkt+sJgmC7bmdYbof1DX2drc6sb2jrbPUFOmedAXbNEBERUQAxECEiIqKAYSBCREREAcNAhIiIiAJGMcmqRERE/mAwGGAwGAJdDLf0ej2MRmOgi+E1nU4HnU7XpmswECEiok6jtrYWgiAgOjraNkolmGg0Gr+OEG1PsixDr9ejtrYWkZGRrb4Ou2aIiKjTMJvNiIiICMogRGkEQUBERESb5/hiIEJERJ0GAxD/a+vPlIEIERERBQwDESIiIgoYBiJECibLMn4+U4X8auVk2RMROeKoGSIF23m2Gv/ZngcA+HLO4ACXhojId2wRIVKwjGJ9oItApGiyLEM21Hf8Px8XtcvIyMDMmTMxaNAg3HLLLcjLy8PKlStx5ZVX4vbbb8egQYNw8803o7Cw0HbO/v37MX36dAwaNAh33XUXqqqqAAArV67Etddei48//hgjR47EiBEj8PXXX/v15+oLtogQKRhHABC1kdEA6f7rO/xlxVdXAbowr46tra3FTTfdhDlz5uDVV1/F0qVLcccdd+C2227D/v378dhjj+Hpp5/Gk08+ib///e/44IMPUFlZiTlz5uDOO+/EW2+9hb/97W/417/+hcWLFwMAjh07hvXr12Pt2rX45JNP8PTTT2P69OntWWWPfG4Rqa6uxvz581FUVGTblp2djcceewy33347li9f7hTpNbePiIiImrdx40ZERkbioYceQvfu3fHvf/8bWVlZKCkpQUpKCubPn4+ePXtiwYIF2LRpE8xmM3744QdoNBr85S9/QVpaGu6++258//33tmvW1dVhyZIl6NOnD2bPno28vLyA1c+nFpGqqiosWrQIxcXFtm0mkwmLFi3CyJEj8eCDD+KDDz7A1q1bMXny5Gb3EVHbsT2EqI20OmvrRABe11t5eXno2bOn7blOp0O3bt2gUqmQkpJiaxlNTk6GxWJBeXk5CgoKUFpaiiFDhgAAJElCTU0N6uvrAQD9+vVDUlKStSharb9q1So+BSL//e9/cf755+PEiRO2bfv27UNdXR1uvfVW6HQ6zJ49G++99x4mT57c7D4iajv2zBC1jSAIXneRBEpaWhqys7Ntz+vr61FYWAhJkpCXlwdJkiCKInJzc6FWqxEfH4+UlBSMGDECb7zxBgBrLkxVVRU0Gg0AIDo6OiB1ccenrpl58+bhiiuucNp25swZDBgwwLboTa9evZCTk9PiPk9MJhPq6ups//R6ezKeIAh+/dce1wzmf6xvaP5z/PvoLHXubO8x6+vfayrNtGnTUFtbi5deegk5OTl44oknkJ6ejoSEBBQUFOCVV15BdnY2Xn75ZVxyySVQqVSYOnUqcnNzsW/fPoiiiC+++AJz5sxpt/SItvysfWoR6datm8s2vV5va95pLIwoiqipqWl2X1RUlNvXWLt2LVavXm17np6ejkWLFjldx5+Sk5Pb5brBivUNLVHHawGUArDXNdTr3BTrG9r8XV+9Xm9rFQhWTcsXFxeHlStX4pFHHsFbb72F8ePHY/ny5fjxxx8xZswYHDlyBG+88QbGjh2Ll156CRqNBomJiVi+fDkee+wxLFiwAAMHDsTy5csRHh4OtVoNQRBsr6NWq92+rre0Wi1SUlJaXd82j5oRRdGl8FqtFkajsdl9nsyaNcspc7cxqiouLm7zwjqOBEFAcnIyCgoKOkUCLesbmmrzc22PCwoKOkWdG3WW97gR6+sfRqMxqFe39bT6bv/+/bFu3TqnbWazGVqtFm+99ZbT9sbzhw0bhq+++spl3zXXXINrrrnGdlxKSgpyc3Nb/XMxGo3Iz8932a5Wq71qRGhzIBIVFYWzZ886bdPr9VCr1c3u80Sj0XiMytrjj0+W5U7xR92I9Q0tcsbvQORQ6+OGeoZ6nZtifUNbZ6uvUrXlPWrzhGb9+vVzSl4tKiqCyWRCVFRUs/uIyA9KCmwPZUkKYEGIqKPdcMMNTqkMStXmQGTw4MGoq6vDtm3bAADr1q3D8OHDIYpis/uIqO2c0sEYiBCRArW5a0alUmHevHlYunQpVqxYAUmS8NRTT7W4j4j8wLE5VGYgQkTK06pAZNUq58lfxo8fj6VLlyIzMxMDBw5ETEyMV/uIqG2cWkQslkAVg4io1fy21kx8fDzi4+N93kdErSfAoUVEYiBCRMrDZA0iBVvV+2L7E7aIEJECMRAhUiiDuUlOCJNViUiBGIgQhQoGIkSd0tmzZ5GWlhboYrQaAxGiUMEcESJSIAYiRAolNZ3JkC0iRKRADESIFMplJlUmqxL5TJZl1JulDv/n65Tohw4dwlVXXYX+/ftjxowZOHbsWIvnZGRkYObMmRg0aBBuueUW5OXl2fZt27YNF110Efr27YsZM2bg9OnTtn1btmzB1KlTMXjwYDz88MMwGAw+ldVXfhu+S0QdrOl9jF0zRD4zWGTcsPJ4h7/uyhsGIEwttHwggOrqatx8882466678Pbbb+Ptt9/G/fffj40bN3o8p7a2FjfddBPmzJmDV199FUuXLsUdd9yB9evXQxRFPPjgg7j33nvxxz/+EUuWLMF//vMfvP766zh9+jTuuOMOLFy4EBMnTsS8efPwxhtv4C9/+Yufau6KgQiRUjWZSVViiwhRSPrhhx8QFxeHBx54AADw4IMPYtSoUc2es3HjRkRGRuKhhx4CAPz73//GiBEjsG/fPowZMwZhYWEwGAyIiYnBokWLIDW0sK5btw5Dhw7FjTfeCAC45ZZb8NlnnzEQISJXkuTcJLI534S5ASoLkVLpVAJW3jAgIK/rrfz8fPTo0cP2PC4uDjNmzGj2nLy8PPTs2dP+ejodunXrhry8PIwZMwavvfYaXnjhBbz66qsYMmQInn76aYwaNQoFBQU4dOgQBg8eDAAwm82IjIz0sXa+YSBCpFBNc0R+L2eyKpGvBEHwuoskUFJTU5GdnW17Xltbi6uuugqfffYZunbt6vactLQ0p3Pq6+tRWFiItLQ01NXVoa6uDp999hmMRiMWL16MBQsWYNOmTUhJScHFF1+MJ554AgBgsVig1+vbtX5MViVSqiZdMztLfUt+IyJlmDp1KiorK7F06VLk5eXhv//9LywWC5KSkjyeM23aNNTW1uKll15CTk4OnnjiCaSnp2PUqFGQJAm33nor1qxZg7KyMgiCYOuamTlzJnbt2oWsrCwAwHvvvWfr3mkvDESIFKpp14yRDSJEISk6Ohoff/wxNm7ciEmTJmHv3r147733IAieW3IiIyPx8ccfY9u2bZg6dSry8vLw/vvvQxRFREVFYenSpVi6dCnOP/98/PDDD1i4cCEAoFevXliyZAmefvppTJ48GRkZGXjttdfatX7smiFSGPnYIUj/ex/441wAYYEuDhF1gGHDhuGrr75yu69Hjx7Izc112T548GB88cUXbs+ZPn06pk+f7nbf5MmTMXny5NYX1kdsESFSGGnx48CZk7C8uSjQRSEiajMGIkQKxYwQIgoFDESIFEp20z9cazQHoCRERK3HQIRIoWS4BiIHcysDUBIiotZjIEKkWK6BiElihw0RKQsDESKFktx0zZjMHMNL1BKJK1X7jT9+lgxEiEKI0cIbLFFzIiIiUF1dzWDEDyRJQnV1NSIiItp0Hc4jQqRQ7nJE2DVD1Dy1Wo3IyEjU1NQEuihuabVaGI3GQBfDa5GRkVCr2xZKMBAhUih3o2aqD+wFpowOQGmIlEOtViMmJibQxXAhCAJSUlKQn58PWe48XyrYNUOkUO5uU1XbvuvwchARtQUDESKFcts1I7KRk4iUhYEIkcIUhnXBsj5XoFQX67LPLKgCUCIiotbj1ycihfnnqHtREtYFP6SMd9lnZosIESkMW0SIFKYkrAsAoEbjOmRuXY+LYKnXd3SRiIhajYEIUQgxqrT44VP3S4UTEQUjBiJEISazqDrQRSAi8hoDEaIQY2HCKhEpCAMRIgWRvJjkyCwyECEi5WAgQqQghwrrWjyGQ3iJSEkYiBApiNHivkVEcGgpsQj8syYi5eAdiygECA4TvnMuESJSEgYiRAriOqm7legQiFiYI0JECsJAhEhB3Cy4a93u1DXDQISIlIOBCJGCCB4iEcet7JohIiVhIEKkJJLkdrNzjghbRIhIORiIECmIkJPlfrvDY3bNEJGSMBAhUhBBsrjdLrJFhIgUioEIkYJ4TFZ1eMwJzYhISRiIECmI4GGyMscARfYUrRARBSEGIkQK4mnUjOjFGjRERMGIgQiRggii+0BE5ZAjAsYkRKQgDESIFETwMLeqyOiDiBSKgQiRgnjqmlHBPr9ITngiZHbVEJFCMBAhUhBPXTNN/5B/y6tt/8IQEfkBAxEiBfE0aqZp10yZ3twRxSEiajMGIkQKIntsEXEORKTK8o4oDhFRm/ltdawff/wRn376KWpqajBgwADcc8896Nq1K7Kzs/HGG2+goKAAU6ZMwZw5czz2cxNR82QIcDcsRtU0EDlxFBiT3kGlIiJqPb+0iBQUFODTTz/FI488gpdffhmJiYl4/fXXYTKZsGjRIqSnp2PhwoXIycnB1q1b/fGSRJ2Tp3lEmjx3vzQeEVHw8Usgcvr0afTv3x99+vRBYmIiJk+ejPz8fOzbtw91dXW49dZbkZycjNmzZ2Pz5s3+eEmiTsnTaJimLSIyANlksj+3uF+jhogo0PzSNdO9e3ccPnwYWVlZ6NatGzZs2IDhw4fjzJkzGDBgAHQ6HQCgV69eyMnJafZaJpMJJocbqCAICA8Ptz32l8ZrdZZuItY3tDVNHZHOnob0p2sgzvkTkNwd0sv/hHjD3RCnXBmYAraDzvYes76hrzPWGfBjIDJhwgQ8+uijAICuXbviueeew7p165CUlGQ7ThAEiKKImpoaREVFub3W2rVrsXr1atvz9PR0LFq0yOk6/pScnNwu1w1WrK+ync3KB1Dhsl0tCk6pI0JtNQBAWvE6xLgEwGKB9MmbSLv5ro4paAcKtfe4Jaxv6OtsdfZLIHL8+HH89ttveO6559C9e3esXbsWCxcuxNChQ6HRaJyO1Wq1MBqNHq81a9YsTJ8+3fa8MTIsLi6G2ey/IYmCICA5ORkFBQWdYvIn1jc0VFZUut8hSfAw6SokhwglPz+/HUoVGKH6HnvC+oa+UKuzWq32qhHBL4HIjh07cP7556Nfv34AgBtvvBEbN27EhAkTcPbsWadj9Xo91GrPL6vRaFyCl0bt8cbIshwSb7i3WF9lkzzUpa9QjSOIsz3fnTgEV+b+bH0i2lPBQuln0SjU3uOWsL6hr7PV2S/JqpIkoaKiwvZcr9fDYDBApVLhxIkTtu1FRUUwmUweu2WIqHme7k1jUiKdnv/epb/9iYdJ0IiIgoFf7lADBw7Erl278PXXX2P79u144YUXEBsbi8svvxx1dXXYtm0bAGDdunUYPnw4RJE3RqJWcROJDKg8A1XPZuYM4d8bEQUxv3TNnHfeecjLy8P69etRXl6Onj174uGHH4Zarca8efOwdOlSrFixApIk4amnnvLHSxJ1SjJkNE0GEcLCm481RFW7lomIqC38EogIgoDrrrsO1113ncu+8ePHY+nSpcjMzMTAgQMRExPjj5ck6pTcd83IUDXXuMkWESIKYn6b4r058fHxiI+P74iXIgptbiIREa7ziDgfwECEiIIX71BECuKuQUQAIDYXiTBZlYiCGO9QRAriadRMc3FIZlhX3DfhUexIGt4+hSIiagMGIkSK4q5rRm6292Vxt4tRGJ6AxUNvacdyERG1DgMRIgXxuOidm7UpfkgeB71KB6PoXSqYtGMzLK8+A/n0CViWPAk5M8P5tctLIe3+iQvoEZFfdUiyKhH5h7s4RIAM0U0g8vqg67A/fgAEL2dolD9YAgCQDuyy/v/wPqje+dK2X/rnfYChHrjhTgjTZvheeCIiN9giQqQgHpNVPeSI7Og60n9/5IZ6axkO7fXXFYmIGIgQKYqb1o3mAhEAEAQ/r1nReZbAIKIOwECESEFkN1GAAABuumYaiZK/czoYiRCR/zAQIVIQT+kezc1n1tw+vxaCiKgVGIgQKYmbGECEDKGZcKOlsKHKYMHWrEqvR9cQEfkTAxEiBXHXNQMBzXbNSC20iSzdmYeXd+Tjnf4z21Y4IqJWYCBCpCCehu82E4egWGNfaFIuzHPZvzu3FgCwKWV86wtBRNRKDESIFMVNsqrsfR6I9I97XbbFauxnF4TFIzMqDc8PnYvc8CQPRWAgQkT+w05hIgVx2yIiAEJzTSIOinVxSG6yrZehBL+LCQCAP038u237magUvN3aghIReYktIkQK4r5rBl43idxz7uMwWSSnbbFSvdtjC8MTfCscEVErMBAhUhR384jIaC4SEWTnwGP14VLn/V4GMet6XIQFYx5EDTTenUBE5AUGIkQK4m7Ru5biCFlw/jM/WFjnvN/L5pRlfa9EVnQa1kUN9ep4IiJvMBAhUhCPaaI+zFpmlpyv4mvqqUFQ+XgGEZFnDESIlMTDhGa+MBnNLV6TiKijMBAhUhB3XTO+MpWXOV/T5zK0uQhERDYMRIgUxF0MIDj81xtnVTFOz30ORLzNbiUi8gIDESIFcTt8t41NFL6e/W3UYDz4TRaMTYYBExG1BgMRIkVxN7OqhHZYY7dZpysM2JVT06GvSUShiYEIkYI0Nn70qc5pstH7dg2NZHK+ZiuDGIm5IkTkBwxEiBSk8bO/aXeMIFm8vkZ3g/fJqrLJCPnQb5CNBq+vT0TkCwYiRAoiN4QNTYfsxoWrMbbkCES55YAkUja1eEyjrZ99CcPSZyB/9IpvBSUi8hIDESIFaWwIcWwRESBDjIzGP4ao8HSX/BavITXpimku13WJOAw3XLQQZw9ltKq8REQtYSBCpCQNUYPgpkNFvGQmIi68tMVLHIlIc76kFy/76Oj7XYti9r5lhYjIEwYiRArSGDSIHpoxZJXvf9LeBCJ6dZjLtu92n/L5tYiImmIgQqQgtq4Zh/BBcIwkmokqBlVmOVzH8cDWjZo5LEW36jwiIkcMRIgUpSFZ1SGQkL2MI7rXFtkebztd1eSKrVNZXdfyQUREzWAgQqQgjfGHCIdZTR0iCZXoOSqJ7J1ue/zyDntSa1sCkWUb9rfhbCIiBiJEiuJp1Eyjib3jMTgpHFcMiHM5VxMR7nwtQz0A11E0vqio5fwiRNQ2DESIFKVh1EyU+/wMtUrEokt7455xyS77wjUqp+eWynLr/9sQiJSJrkmsRES+YCBCpCC2UTOt6FC5NMF5uK3FbO3ekdqwmu4pVZdWn0tEBDAQIVIU+6gZR94FEpGQsHDvq7bnFqMRACB5yHa9T3cGT/bjXCFE1L4YiBApirsJzbxrHREiItCv6qztucloDTIszbSIqGLjXLZNLP7dq9cjIvIGAxEiBbGPmrGrE7XendxvMNSXzoQgW7tkLCYzAM/JqpFmPar1ri0if78gDfOHRXpdZiKi5jAQIVIQ2+q7DtsOabt5d7JKBfHa26FqiGbMpoauGQ+ByLi9XyE+Wue07ZozmwBBxMAEa5JqjLHG67ITEbnDQIRISdzMrOp14qpg/XNXw7pCr7mhRcTdqJnXfl0E3U3zMLhvqm3bY6a9uDmqFBgxDkLDfCWyIDSZpZWIyDfqQBeAiLwnN86s6rAtzqL36lyhIRdE3XANi8na7dK0RWR+xiqkJMVCGDUBAPDiZb1RbbTgnJRBLteSAUCWAMF5aDARkbcYiBApiLu1ZiJk30a2qBpyRMyWhuG7DYHIzbo89NzzHcaVHoX43Nu24/sluM4VIojWUEiGAEgSIDIQIaLWYSBCpCDuckTMkrsjnS3c+ypws3XorrphevjG8xpPH5qgxZB5dwBR0RCSXCdEc2QLRISGQISIqJWYI0KkKA3Ddx0iEclsdnvk+T2ts68OKz+JgV3to1wau2bMkvX/jS0ioihC6DMQQtdUtERoaAGxtYgQEbUSW0SIFER2M/mYWXD/feL+ickYG2nA2KMFEOf8zba9sWvG0tDP05isqhK9/17i0jVDRNRKDESIFES2TWhmJ3lIFI3QqDBldB9gdB+n7bauGUtDi4jQGIh4P9W7LVlVEKzJqkRErcSuGSIlcTPFu8VDi4gnqsaumYZr2btmfFhzxtYiAkDi8F0iaj0GIkQK4jZZ1YcuFQBQmwwAHFfftZ7vS9eMSuWQrCpbfHp9IiJHDESIFER2M3mZp64ZT0wNw3YLD2dYz29Ni4itPMwRIaK2aZdA5OOPP8bzzz9ve56dnY3HHnsMt99+O5YvX86ZGIlay03XjKdkVU+KdXEAgFJdDAD7oneiDy0iTjELAxEiagO/ByLZ2dn4/vvvcdtttwEATCYTFi1ahPT0dCxcuBA5OTnYunWrv1+WqFOwdc04Dt/1MRAZXXYMAKDW6hqu6XuLSGOyqiSIDESIqE38GojIsox33nkHV1xxBZKTrRMi7du3D3V1dbj11luRnJyM2bNnY/Pmzf58WaJOw11josXHrpm41BQAgJTSw3rNxh2C94FIY8wiCSJkC3NEiKj1/Dp8d9OmTTh9+jSmTJmC3377DSNHjsSZM2cwYMAA6HTWb1+9evVCTk6Ox2uYTCaYTPYpqwVBQHh4uO2xvzRey5/XDGasb2gRAOgsRhhUWvSuyYMgjPS6zqIoABZAkuWGYxuG76pUXv+81A7dOLIXr9keQv09bor1DX2dsc6AHwOR+vp6fPbZZ0hOTkZpaSl+/PFHfP755xg4cCCSkpJsxwmCAFEUUVNTg6ioKJfrrF27FqtXr7Y9T09Px6JFi5yu4U+NLTedBeurbFqdDqgF1GoV3h6lxSff7MQdF49ASkqK7ZiW6hwWpgNMgKhSIyUlBRIOAQC6JCQ4Xac50QYzgOMAgPguCYj08rz2EGrvcUtY39DX2erst0Dk119/hcFgwJNPPomoqCjMmjULDz/8MLZs2YJJkyY5HavVamE0Gt1eZ9asWZg+fbrteWNkWFxcDLOHqaxbQxAEJCcno6CgoFMkz7K+ocFQXw8gBhaLBV2GDcafBvWDoNYgPz/f6zo3/h0ZjUbk5+fbtldUVkLj8Lw59Q4L3BQWFiBc57owXnsL1ffYE9Y39IVandVqtVeNCH4LREpLS9GvXz9bK4dKpULPnj1RVFSEqqoqp2P1ej3UavcvrdFooNFo3O5rjzdGluWQeMO9xfoqW+PHv4iGeqnULvVrsc4Nwf3Xqt74Y7UBtlnjBcHrn5XoMIzYbLEE9Gccau9xS1jf0NfZ6uy3ZNXExESXVo6SkhLMnTsXJ06csG0rKiqCyWRy2y1DRM1rmJXdNjtqa+RLWtvjeV+csiW7Cipfhu/a+7AlC0fNEFHr+S0QGT16NHJzc/H999+jtLQU69evx+nTpzFy5EjU1dVh27ZtAIB169Zh+PDhPs1ZQERWlobmC9/GyTjzNIOq4MMwYMeRvj8WcdQMEbWe37pmoqKi8Pjjj2P58uVYtmwZ4uLi8OCDDyI5ORnz5s3D0qVLsWLFCkiShKeeespfL0vUqTS2PajQ+lYIj4GIL6vvOrSIvHNKxqTRFkTp2hIeEVFn5dfhuwMGDMC///1vl+3jx4/H0qVLkZmZiYEDByImJsafL0vUaTR2zbSlPVGtcj800JdApCm9WWIgQkSt4tdApDnx8fGIj4/vqJcjCkmNXTOi0PocEdHDHAW+dM00JevrgMjYVp9PRJ0XEzWIFKQxG6MtbQ8e50pqxaJ3NqdPtHwMEZEbDESIFESSrC0hqjYEDUbZQ4uID6NmmvrTUR3qSopbfT4RdV4MRIgURJLbHohER+jcbm/LpNImUYOPvj/YhisQUWfFQIRIQSwNLSKe8jy8cc3Evm63tyVHBAA2yKltOp+IOicGIkQKYpvQrA3dKHHh7nPUhbbkiBARtRIDESIFkRoDkTYGDXel1LtsE9rUOUNE1DoMRIgUxDaPSBtnJp4wbpDLtk628jgRBQkGIkQKYvFDsioAxEe6JqwKPkYiL/UswwX1Z2zPJxQzWZWIfMdAhEhBJD+1iKhFAYsv6+W80cdApO8F5+HhOy/FnX2ts5qY1doWziAicsVAhEhB/JGs2qhPlzCn560diRMTab2OSeAU70TkOwYiRApiaUgo9cfq1S7dO63s7tFoNAAAE1SQpdYvxkdEnRMDESIlacgREf3QItJUa5NVNTprIGIU1YDJ5LJfliyQqyraUDIiCmUMRIgUpLG9wdfEUm+09ppanTU3xCyqAZPBZb/04j8hLZgL+Uym0/byGj3e/nI3srLyWvW6RBQaGIgQKYjc0DXTHiNtWx2IqK25IUZRjaoaPUrrmrSKHD8EAJC3b3Ta/PoPx/BNdTT+sqPK9Rwi6jQYiBApSEPPTPu0iLQyvNGorOcZRQ3mbirHHWsz8c1Ph2E0W5ocKTs9y6qzv95jG7Nb9dpEpHwMRIgUpHHh3PaYfKy1U7xrGs4rDYuzbXs7W4XbVx5xPlB2DkQcR+kU1rBFhKizYiBCpCCy3Ng10x6RSCu7ZjwkztZAA2NhvueXa9JCQkSdk/vVr4goKLVni0hrM08au2bc+fXrTRATh+JkdA/cLDt/89EJDESIiIEIkaI0fnT7a6HcXjX5OBOVYn3SymtqmwlEdlWK+HHYrQCAftJBnO/42moDzljC3J9IRJ0Gu2aIFES2RQv+iUTOj3XMzfB/i8iP3UbbHpfAeX0bncgWESJiIEKkKP5uEYkYOMTh4q0LDDRNZnm9wnDS7XGOY2h+PVuNjfo4p/0mC2dlJeqMGIgQBSmDWYLeZP1wLqwx4u3dBShURVt3+ikQ0UU4dI00TNXuK7XDXeS8ogO4Y/pYXJL3i8txFofbzXM/5rrsf+S7My7biCj0MRAhCkKyLGPO6hO4cdVxGC0S/vNTHr45XoFqlTVwaO0CdU2ldYmwPRa0umaO9MxxTpN5k/pB0zUZf7rhQpfjWmrvyCp3nZWViEIfk1WJgpAkA8aGpXYLa0w4VV7vtN9fo2aGdo3Anycmo3ts64KQRu8NM0Cfn4u4UZcDAISefTCzfy7Wnah2OKpdhvoQkcKxRYQoCElN8jViw5y/M/hzHpGpfeMwMDG8TddIHDkSPS67wql1ZM6YVKdj5FbmoMhmc5vKRkTBjYEIURCyOH5myxK6hKmc9rfPPCL+pW5yd2lNGCJ9/Cak+6+DXFzglzIRUfBhIEIUhBxbRITjhxHXji0i7cVlPRzJ91Ex8tb1kC0WyBs+91OpiCjYMEeEKAg5fmbLRgPiwpXXItKUZGm5i8VolqB1aEp5Y8A12Bc/AC/iIOLasWxEFDhsESEKQk45IpIEXZP1XJQYiKBe3+Ihf12fhY0nK1BrtM46sjF1AkrCumC90L29S0dEAcJAhCgIOeaI7KnVuux36fYIUhdF24MP2Whs8ficahNe/bUAN/3vBOrN9mYhi6yM+hKR7xiIEAUhxxaRD8tiIRU4TwCmlI/l2ZOH2h47BiJdhZbnDHl3tz1BlZPBE4UuBiJEQcjSZLpzKTPD6bm/JjRrb9E6e25LtWCfubUrrC0lo8qOeTx346kq22MGIkShi4EIURCSpCYfvRFRzs8VEohEauy3mHqVfdK0xgafCcWHvLoOAxGi0MVAhCgIWSSL03M5KtrpuSgo46NZEASIsrUuSQ6pLo2Tm2kGDnV3mgvJyxwRuaQQ0g9fQjbUt3wwEQUFBiJEQahpi4gsNP1TVUaLCADMjKsFANSHRdq2yZUVAACNyssAw8s5SKSn/gx55buQ13zkWyGJKGAYiBAFIcnSJBBp0gCilBwRAAjXWPNE6mX77UZqKL/G1PJIGgD4UtPHuxczWHNP5IzffSghEQUSAxGiICTJTZJVmx6gnDgEuoYJygwOtxuz1rq2jbZ3ekDKRETBg4EIURCyNGkRkZpEHnLTZNYg1tgioneYyNksWh+rVSqX42ef3dz2F23lAntE1PE4xTtREHJpEZHh1Aribc5EMAhr7JqBPegwN+S8qEUBV+ZsR354Au44+RVKdbEYFmFCFQbgGzjPpnoqKx990lM6ruBE1CHYIkIUhJomq7qGHcr5xh+mtX7fqRfs33ssDbcetUrA3fddgydmjECqvgTDKzIhSBakjRjmcp2/7qiEydJ8ALY3fiCeGnk3ijTRzR5HRMGDgQhREJKafOC6hB0K6poJi7DOH1IPla0lx94iIkKI6QKhV1/7CRYLLuyf4PZar313uNnXembEnfi9S3+8mjzNDyUnoo7AQIQoCFmatogoeK2V8ARrUFEvaoD8swAcAhGHxfyEiZOs/7/yekTp3PcabynXuN3eVLk6suWDiCgoMEeEKAg5rjVzXtEBmBKb5EbICsoR0VlnMqtXaQGjdY0Zc0O+iFMgcvuDEK68AeiW2uyifrJkgSC6Jrk6HaOkYUVEnRxbRIiCkOSQjCpDcBk1o6RRIWENw3frVTqgadeMw4RmgqiCkJzmEoScV3LQ6bk5O6vF18zTdWlTmYmo4zAQIQpCjl0zsiC4JKvKCgpEwhsCEbOohsncGIi4toh4clFqGP5YvMv2vKK0sh1KSUSBwkCEKAg5jpqRICg6R6RxQjMAMJglyLIMs9hyIDJZV4G+plKMvnIq7rj7asQZqwEAmyp1Hs8hIuVhjghREHKcR8Rdi4iSRs04rifz0VkB9w6273M3oVmjv1w70em5SrRe59N8NQbk1WBMGofoEoUCtogQBSHHFhG3OSJuZhZRgo2lKpgd6qZWe38LKlVH2R4fKdK7PUYjmVpfOCIKCAYiREGopRwRJSWrNmUym22PvckRcUfyUH+TaB/ea1ZQqxFRZ9Yugcizzz6LrVu3AgCys7Px2GOP4fbbb8fy5csVlWRHFChOLSLhkW5GzXRwgfxIX1dve6xqZSBi0btvEXFUa7S06tpE1LH8Hoj89NNPOHDgAADAZDJh0aJFSE9Px8KFC5GTk2MLUIjIM8dv/JLsJu5QWED/YHK17XFljTUQEeWW5wPxxHLiSIvH1NTWt3gMEQWeXwORmpoaLFu2DKmpqQCAffv2oa6uDrfeeiuSk5Mxe/ZsbN7sh5U1iUKcc44IYGnSIhItmqEkU6aOQ6TZ2opRVVYBABBlGWgmWbU5mSb3I2d61xXaHu/Z3fx08EQUHPw6ambZsmUYP348jEYjAODMmTMYMGAAdDrrTaNXr17Iyclp9homkwkmkz3hTBAEhIeH2x77S+O1/HnNYMb6KotjeoMM+0yhl+T9gj7VuUhNn+hSt2Cvc63a+nf8+al6AFoIkCGoVK0q75HwVGRkFSIlJcXpfJVs7455vzQWM4P0Z9Eawf7++ltnqy/QOesM+DEQOXToEA4ePIgXX3wRH3zwAQBAr9cjKSnJdowgCBBFETU1NYiKinJ7nbVr12L16tW25+np6Vi0aJHTdfwpOTm5Xa4brFhfZYiILABgDeglCJAbZiKdWHwQo8pPIHnK01DFJ7o9N3jrfBQAcEhvnfJdkGV0S06Bqov7Be48nd/obz+X4Zwzv+GFmSMQG25NUpUEeyPvkDATUlKaTI0fAoL3/W0fna2+QOers18CEaPRiHfeeQd33303IiIibNtFUYRG47xIlVartbWYuDNr1ixMnz7d9rwxMiwuLobZ7L/maEEQkJycjIKCgk6RQMv6KktVlT2nwgQRloY6qKdOh+rCCSgymID8fKdzlFZnAUBhcTGEes/3A0f/HBWBf++vc9q2L6cSSzYexrxx3QAAklpr29dHrkR+k5+Rkint/W2rzlZfIPTqrFarvWpE8EsgsmbNGvTt2xejR4922h4VFYWzZ886bdPr9VCrPb+sRqNxCV4atccbI8tySLzh3mJ9lcHisNaMSVDZRs0IogiERzRbp2Ct8+zIEnxaa2/FEWQZsih6nXg7dmhPPPHRAvxr5N1O24uq9Lb6Oo4uksyWoPw5tFWwvr/tpbPVF+h8dfZLILJ9+3ZUVVXhtttuAwAYDAbs3LkTSUlJsFjsfbZFRUUwmUweu2WIyMricBMyCaJtHhFRyV3HKufbjQgJEH3Llz+nXzIePrwci4feYttWd/YsgF4AAMlkBBoaRSSLshJ6iTorvwQi//rXv5wCjuXLl6N///6YNGkSHnroIWzbtg0XXXQR1q1bh+HDh0P08eZD1Nk4NIjALKhsyaqioNy/HSk8AqiyPxdkGRB8GzUjznsEfzAYsHi1fQXeQ+pEZOzYjUHnjbPl0gCA7HBPMpglfHuiHBO6RyMlWgt/s0gyCmtMSI3x/7WJQp1f7moJCQno2rWr7V9YWBhiYmIQExODefPm2fJHfv31V9x8883+eEmikCY5tYjYu2ZEQbnNtdFR4U7PBcg+t4gAgKBzHbr7aFY0ZLMJksNoA9khmvvk9xJ8sLcY87865fPreeOlHXm476tT+CGzol2uTxTK2mXRu/nz59sejx8/HkuXLkVmZiYGDhyImJiY9nhJopBineLd+qFqFNXQNgYiCm5NnDKqF97NtAcCoty6QMST2sIiW8sR4JxTdqTImuRqaac4bvsZa3Lx6sOlmNY3rn1ehChEdchdLT4+HuPGjWMQQuQlxxYRs6C2J6sqOEckMkyLS7UltuetbREBgNemJGFuVInTtqL8YhSG24cCHzVFQN67A9UGC46XcpZVomCl3K9XRCHMcUIzk+jYNaPgSASAxqH4giy3euKm7ikJuHbmBfj8zPtIqysCAPya77z+TE5kN8w8Go+nNp91d4l2oex3hygwGIgQBSHHKd7NTjkigSqRf6gcKiD6YeW+7ks+RLLGmpRa6KHR42SZ8w7ZYGjz63qm8DeIKAAYiBAFIclhxIfsNHxX2R90KscWET8EIoJabZuXSCuZWjjaylBV1fJBrWX2rgxEZMdAhCgIOQ6HB2AbDaJSeJOIyiGQ8tcAIHXDXcxskZo/sEGN3ruZXFulvKTlY4jICQMRoiCUb3AOOCwNf6qKbxFxuOP4o2sGANQNPxOTl0Ni/LlUhAtJgsFswX/X7cGOfSfb73WIQggDEaIg9GttmNNzi9AYiASiNP4jOlTAH10zgL1FxOhdgwjMxvYLRATI+Hp7BjbXRmHREc7sSuQNBiJECmBumIFUVHgkonIYruuvQETT8DMxNlwu3FyPkWXHPR7/W261x33+sCzXt9liiTo7BiJECqLwOAQq2d5s4bcckYYM2FrZGgCYRDWe/P1dPL3/LbfHv18QhtLy9klYLdXFtst1iUIZAxGiINS/6ozb7YKC15oBgH3l9kAk32HysbZQN7SyHNN2BQCYResommEVmbj7+Fq357y7fr9fXrupepXz9PMVhUxeJWqJsu9qRCFK8vCnKWraZVWGDpOj8v/symqV+2YiAcDleTvx8u4XsTC52GlfSU07jpxxsHbNpg55HSIlYyBCFIQkDy0fYteUDi6Jf700vb/fr6lWuf6shPEXAWHWRfZ61RZiyNQLnPYn1pW6vZZssUAuzPNb2XQ1FX67FlGoYiBCFIQkjfvl5MUoZa/XFBcd1vJBPgpXu7aIiHcvgPj8u0Dv/hBuuAsAcP3pjbb9XevLAABH8yuxavtx2xwk0tsvQPrHvZB2bvGpDJ4mU0vqm+7TdYg6IwYiREFIVrnvglG5+fbf2fWNdM56nXNqPQBAiIyG6v9ehDjtjwCAm+6chWtP/wAAMKisgd7fN+fj4zMSNm/5zXry3h0AAHnDGp/K4GlOFJNK49N1iDoj3tWIglCN6L5FpLWLxIUyTX620/Nu48e7PU5I7o6IKZcBAOp00U77ijJPOx8seTkpSQNPN1Kjl5OsEXVmDESIgozRIqFMFel2n9LnEXGUUF/hl+uoLM4Th41M8tz9ExlmDfBydPHILKm1bY9Gk+RVyXmK/ZY0bRHRWqxdNSaJgUhH27/7MLat3wZZ5s9eKRiIEAUZvcn+bVyUnT8QxRDomrk47xcIsoT7Tnzul+sJ/Yc4PY+JjfJ4bGSYtaskM7o7HvrurG27RmPdbhDV+DVxKPTwbVIyc5NbaZTZGuQYjh+FnJnh07WobZ48rsJL5d2Qv//3QBeFvKT8uxpRiGn8IifIEsQm3+pEUfl/svNOrMN7O57BaNn9yBWfpfawPRxbcgRI9zwyp3HSs6bMuggAwLPD78CiYbfixe5X+lQEuUmXmVayttIYRQ2k5//m07XIP8o4h4tiKP+uRhRiLA3BhwDZNRAJgRwR9U3zEKeRId73d79czzGx9wb9YQii59YMk+B+n1FrHep7qEs/AMDemL4+laGrpcbpeSysXTMGJqsGjo/daxQ4yp4diSgENYYeoiw7rcciylJIJKuKk66AfNHl/quLQ+DR45KLmz10bPdovLu32GW7qY3pBDKsdRlbcgTR5jr06BqLYwD0TWZapY5z1KDDsEAXgrzCFhGiINPYCCLKMgTZORAJFf4MqATYr6Xq3/xHT7co9y0UBkvTZFPfZl5tPHtmogF/HhqOqHTr/CHbksfgRHQPzyeSXzkmqK6o6xbAkpAvGIgQBZgsWSC9vwTSjxsAAJJj1wwcE1c5CsCdnnE6xIepMDhBB22050RVwNq1tXiEgHNKrQmkkwv2AACMTWI8dUOScEVJGZ7/aAv2/nqw2es2ni72SId42TWI0NhvrY+OecCH2lBbcJCSMrFrhijQ9v0KeedmYOdm4MLLYGm4mwpNW0QQOi0i/qRTi3h7Zl+ovRza3H/4QDwp1EDe9S3+130YYAa+QXfcaLDnFDSu9fPRhv3YqU7FzpPA2vGy2xwdaccmyCYdoLYnE4er+R0vEBwDEcHDJHMUfPjXQhRg5swM7OsyAPqG2T5ls3XEhQgJjh97AltEPNKoRJ+6e4RhYyDe8VeEOQR3b2w8antcr9ZBrtcjU7LP52Iwu//5yx/815Yj0jjPS0Q4c0MCQXL4G+kh6ANYEvIFW0SIAuzjTAPWjrSuh7LOZIJsNACwdsU4flPwNI04tUFiN6DA+nB/qQlaETCK1tuivqQU4YI9ULGYTIDGNcCQAVgaFilsDIbCE+IB5NuPkeWQSDQOdo5/ITlSGA6dyMOw/qkBKw95hy0iRAG2tudk22PLmUxIDQuwCZBRpYmw7WOOiP/lC/afb7/qHGhl+yytBqMJvVT1tue/HMh0e41nh9+OkrAuAAChoWsmIjLc6RijyexyHvmfY4uIJIj4v11VASwNeYuBCFEQqTFYYGkIRFRN5kFgjoj/dUmKtz+RZRgFeyOxyWRGis7+M990vAxydaXLNfYmDLY9bswhidA4z1fS2N1G7YvJqsrEQIQoiFQZLLA0LLjWtCuGLSL+N3OwPRD5PX6ArVsGAPaXGCE5dMWUmgSseGUFjHt/8Xg9oSFHJFzjfGvlW9cxLIxEFImBCFEQqTNLkBpupqom84ZwFID/6dQiHhjgfrbVotxCp6b+wvAErO41FWs329cwabqwWrza2orlOoKH711HkC2us6ly8bvgx0CEKIhYykrZItLBdBHhbrdvromERXZNMP05uj/MDcFi0y/gsYOGuBwP8MOwo8jupnVnt1jQYyBCFEBNP6BMm76254g0aRFRMUekXfTvkeh2e6kuFrmVBpftZ6JScOPHh/Dl2i1OoeLkwt8ghLkPatgg0jEsX650s9HU8QUhnzAQIQogc5PYYnWvKR67Zjh8t30kx4RhupDrdt+P6jS3202iBu/VpSD/mH0kzc1Vez2+hsz3rkNYdm523cgWkaDHQIQogEySc7BxsEt/SB66ZgR+lrWbCy88x+l5hNm7ybDu32v/kIuafZfnA/nedQjnKQAbMBAJegxEiALIbHH9hLI0bGu6yB2H77YfTbhzl0qiqcbnawhpzovbXVB70vaYOSIdQ3YzaZxsZtdMsGMgQhRAJjfDDaWGAEQFGTOzt9q2s2um/cSEO08y7etPWpAlqNXOo28euGai/XoMRDqEWXCdLLy+noFIsGMgQhRARotzK8dYlNjmQhAhQxXBmVU7QmKExun52fAkp+fd9KVOzyfn70Z6tT2vRC1boFI5fwiK0bF+LiW15GxkN5dtlQxEgh4DEaIAMjWZ+rtnbaFTsqrYvbdtH1tE2td9x1YDAEaWHXfZt+TGc7BkfDjWJB7FqiP/xYPzpiMyXGvbbxI1EDTOwYxTJ0GQvXX51Ub8e8tZHCmqC3RR/Oo/w+a6bKs1uBnSS0GFgQhRAJmMzoHI55FDkVltbSWpUYdB5fBpxiXT2teljy/AGu1OPH3DWJd9EVERSO/fC+pLZ0H37BsQYrpA55A9PEfrZtSNQ75CsI2aeWF7Hvbk1eKxjdmBLkq721HEZNVgx0CEKIDMbhZDW51r/dA6G5YExwk62SLSvoSwcKivux1CV+fVWt/4ZaHb43PFKNvjc/sluR7g8N4FQ46IXF0FuSgPAJBZVt/C0aFjTV6gS0AtYSBCFEA7cmub3S84fJpx1EzHGVF1CgBw/emNSPn7v9weU+Cwcm/akIEu+wXHERxBsAaK9NAcSP93L+SykkAXhciJa4oxEXWYdZnN99GLDl8V3K+IQu3hH9eMwdn9h9DnunsgaLUtHi/odK7bnLpmgsjpEwC6BLoURDYMRIiCmFPXjMwWkY6i69oN/S5xHYHh6Jw4AfsqZJwTYfTiioEPRTKj0lCmi8GEIOgmag8yJy5TLAYiREHMse+UyarBZcG0fvg5uwrn94xxu190aM4KhhyRR8Y+CAB41VAe4JK0D1O957wXSZKc3g8KLnxniIJMYr31g+KBoyshCo45IoH/MCO7aJ0Kl/XvgmidN51m7t+7nEoDntiUjcPtPIxWclhKoNgUmrf9zCLP+VbGutAaphxqQvM3kkiBhIZvzWbB+sEWa6p26ppRMRBRHKGhO81Tr9oTm7JxoKAOj2/MbtdWk3qDvdtCG6K/R2o4zxcSZ6y2PS45eSooWqXIPQYiREGiscWjQmdt6tdIFogOkYgOnJhJaWTBeouVjQa3+0v19vf075/sgiTL+L2gFtV+noSrzmCfXbTp6KvSOpNtNl8lcwzUZ2dtwAeWH23P5x/W4v0fM92dRkGAgQhRB5Nk2ampvJHQ5JtqnTrMqUVEKzMQUao9W3e1eEwGYrHk28P456azWLDhtF9fv85hmvOm6yzesTYTr3261a+vFwiSQzClkiSIdy1w2v9lDpNZgxUDEaIOJMkyHl62E499sA1Skyx/dZNvqmXaGIgOKarZ2vgOKSP5X6HeuxFP28qt4wcKa0woz8332+ubLPYg9h+5rr9Hm5Dit9cKFItDcG8SPY/DeP+3Qny0r8hlu5yZATk39GeaDUYMRIg6UFGNCZnqeGSEpcBwOhO9aqzTPt528iuXFo+0umIIDk0iWRoGIkpVVq33+ZxDGza5bMs/moHcH3/y+VqSueXWNNmi7BY3k0NTT2MgopOdg/1yvRlfZJTj8yNlqDPa6ytXlEJ6/m+Qnrq/YwobIHJOFixvPg85PyfQRXHCQISogxhLS3DPl6dsz82SDKkhhyC9Jg/aJjkgwytOOo2aIeXakjwWn72/Fru/+t7rcxaHjYXc8C3/cE45Vv2YgT9+nYf7shNRtHuPT68vWVrulnjjjTXI3OZ7kBMsHPNcjPHWOWBGSM6rJmcVVtoem7Oz7DuKCvBL4jBkRSq/Zag50vN/B37bAem/TwW6KE4YiBB1kKc+P+D03CgB9SrrrJ06ixE6h6EVYRYDVIveh1rX8qyepAyf6gbjmaqeOLV7n9fn1D94C0zFhXh8WyFWZNs/aLf9ctin15YsLXcNfddlBB4+q9xWN8dAZIL+DADgtnN7Oh1z4qg9+DDp7fOOHK+W8Z9hc7Fg3F9De3SNoaFlrtS1ayqQ/BaI7N69G/fffz9uvPFGPP7448jJsTb9ZGdn47HHHsPtt9+O5cuXh/abTNSMwxFpTs/v+A0oDrPe+HVhOqd5QrQWE9AlARHpfWzbLs77pWMKSu1qyV7rPDHe3AtvnPhPfPX6cpfthQ5zd8kG9yNyHFm86JoBAElQ7kICpoZgS2cxYqjR+kEb0y3R6Zh1JfbA3uSQU5Ln8PM8eDY0J3wLZn4JRAoKCvD666/jpptuwptvvonExES89dZbMJlMWLRoEdLT07Fw4ULk5ORg69at/nhJopASfv2tOKuyz9Cpk0wQBAFR4fYb5xRNWSCKRn52JioV9adOoLrW/un3QOk2xBhr3B7/Ub/pLtuyI61dD8s//wl3r/gN5Qf2Ib/aiKPF7ifuktzkfzjOs+EoJ+tsi3UIRpaGwC69Jtc2DbFG7Zy0Wifa/55+r7J3e4aJ9qDwq8378cvZavx1fRbOVhogGwyQq6vaseTkl0AkNzcXs2fPxnnnnYe4uDhccsklyMzMxL59+1BXV4dbb70VycnJmD17NjZv3uyPlyQKKWHdnPumNQ2Jq5Fa+zdUde++HVomartLE9znZtyyvR41B+xdNCnTZyDMi3li5uusozoqtNGQZRmr9UkoDovHyi1HcO+Xp/D377NRWmdyOa9O77weztVnNuOdHc+4fY2Va5WZJ9LYNaN2aOnQqD238Ox0SB8JdzhslyYVC3/MxalyA+7/OgszV2dh5pd5KCkswTPfncChvErXiymESVBha7fRKNNGB7ooTvyy1syYMWOcnufl5SE5ORlnzpzBgAEDoGtYmbJXr162LhtPTCYTTCb7H5IgCAgPD7c99pfGa/nzmsGM9Q1uujDnXBAVZGuLiEMgohKEZuujtDq3lRLqO//y4dj52RFUmZ3LaFRp8MGePCDR2jU3tE8y7i4qxvID+bhTzETi+ImYf8j59rygr4TzJ1yB1z45hDJdDH7aa5+g67eodNvjspwCJA50zo2oqDMCsP+OxZhqoX3mdWh+qISpyfdRWa0Nip+pr++vuTEQkc1Aw9+KWqvF8PITONilv8vxo80FEIQRAIAvClv+Tj5/YwHqBTV2b8nHmhujmw1yWqu9f6eXDJ6NnV2tdf4yCN7jRn5f9M5sNuOrr77ClVdeiaKiIiQlJdn2CYIAURRRU1ODqKgot+evXbsWq1evtj1PT0/HokWLnK7jT8nJye1y3WDF+gbSUY97evXqgUvy1uH71IkAADVkpKSkoIvJAuAkAEAbFY2UlJaz+oOrzu0v2Ov73q0xuO69XwEAE1Xl+MXSBQCwK3GY7ZjU1FTMvDoVM6+2PpeNBjy88l4sHnoLAODKoj248ZG/wSxJUEkWmEQNXsywt7YUhduTTPV7dyFl0gSnMtTLh5yem9MHIfWccfisVy3W7z2NsOhIxFaX4Ll9VfgpYRju11uQHB+NiLhY//0gWsnb9zcsLBtALVSyBJ1Wh64Nfytv/uWPOHQiF/fscO5ekSHa/p72Vnn+22xUL9g/LutKqzBo1BAva+C79vqdbgxCACAlJQVmiwS1KvBjVvweiHz22WcICwvDtGnT8Nlnn0Gj0Tjt12q1MBo9L5s9a9YsTJ9u7xNtjAyLi4th9uMyz4IgIDk5GQUFBZ0igZb1DayWptAuLiuHqntvNM5pJkJCfn6+U9nL6wzIz/c8yVWw1bm9KaW+OgCfXtMHKn0dxOj+uHblCZdj3L2vFzz4J1wAAcYuidDpBqOgoADJyclINlcjVxvn8fWOl9ZhZJPr5VfUArDmICWZqzFp6ljk5+dDA2DGUGsQk1MaA8D6YX3DmuMAgBf769F/wmif6+wPvr6/lZVVAFRQSRYYtVqnn2l8z2SgSSCySx+GGc38PTWnND8X+d26tOrc5nTk7/QFL2xEPVR4bWo39Ehpn9FSarXaq0YEvwYiv//+OzZu3Ihnn30WarUaUVFROHvWOfFJr9dDrfb8shqNxiV4adQeb4wsy0F9E/M31jcwDObmh0/KKhVUGg3QMABCLUu2cp9b9DuyI7thYEK9V3UJljp3FCXUNyJMCzR0vz2ZUoan851v/G7L3806ykrbZH+qWI/cZl5redw4XCNJti9x8uF9KD99Bkgajnmmw7jytmvcvmZql0iXay04EY7/9cqDtlvg5tfw9v01mcwAVFDLFgg33u10jtbNt/6DYkKrf29qK6tdzn3/t0JIAG4ZmQSdum2tDB3xO10Pa9fS+1/+iifmXdaur9USv7XJFBYWYunSpbjrrrvQvXt3AEC/fv1w4oQ9+i8qKoLJZPLYLUMUqiwt3FQEQfC40u4jR1Zg6e4Xoe3Tr72KRx3onIsmYELxQdvzZH2JT+enhrV8zDcbrEO95fo6SEueRKnO2sUSF+75S6CnvIRdv/o2Z0mgNA5RVkdGQoj34lu4l2s3dYPrrLgrDxQ5zURbZ7Lgi4xyfJVRjutXHscnmw65nBNIcl0tLC8/6Xbfb5G9UVdU3MElcuaXQMRoNOL555/HuHHjMG7cONTX16O+vh6DBg1CXV0dtm3bBgBYt24dhg8fDlEMfJ8UUUeSmumaeRTWm5ZKcFi0y2HdGfGZN6G6/58QBgxzOZeUR1CpcNXkkbbndwmnmjnaVWqMPen0odpf3R7zTlkX3LDiEB786FdcPek/OBljTV6NbCHHaO1NA/HkEBFLeldY57IBsLNGGZPqmRsCA5WHgEpssqjkQKP1w/fMV1/atvWpdh5M8VxqCZ6bPggq2blF8/f4AZj52QkUHz4CAGg6X9zKAjWk+nq0Rm6FHn/bcBoHCmpbdb478lefAUc8T6T37caWF2VsT36JCPbv34/c3Fxs2rQJc+fOtf0rKyvDvHnz8M477+Duu+/Gr7/+iptvvtkfL0mkKI03SVGWMEfIctrXb/gA6z7HFhGHG5/QLRXCyHHtX0jqMHFJCbbHpQbvFsRrlJpoTyDtOc5z/ka9oMaZKOfAo0fP5pMgRUHA6HMGIP38iUhXWVsCtstdIUvBvw6NuWGtGU+9Ii9d3huXpGowL8UaIJiMRhx/cRH+XDXAdoymYYRno7DoKCTGhuPzOUPw1/NSkKRyHhq9b/MOAECt0fXn8+Pa71pVj7nLdiGjRI8nNvlvPhe5sLnOPGAZAjs1gF9yRMaPH49Vq1a53de1a1csXboUmZmZGDhwIGJiYtweRxTKzEZrorUoS7j6qvMQtWUf3qy2fihExVq7Kh0DETWCO+eB2iY+IQaA9Rv5QLnCp3M14ToA1t+n1O7JEA8X29Ysas7IipOI736p169jcPieWrVzO2LPv8incna0xmDfUyCSHh+O+ZP7Ys8ua1fT8eieeCTaeZjzCKkEx2Dv1ukSH2d7PCk9FsNjeuKODfYE16/iRqJgVy4iNa6tMB/pu+Gc4lLEOgSd3qgyeA76GvNGfB3eW1VnwH9G3dvsMbLFAkEVmJl1O6SPJD4+HuPGjWMQQp1W48yWKlmCGBaO0ZMn2vbpEqw3PsdbQNNmZAotkToNFg+sx1OlG9H7mut8Ord7pP03RRumw1uXJONyIQ8XGc/gHwNdW1cGVp7G0yXf4YlL+0FQux8I4E6PGPuxc093Q05+aTNHe0euroJc3T4Tglka+kfULXxIa7p187hv+tjeTs/j+zg/T0iIxbrZA2yz0maL0VhzohrLjrjOvFqmi8Pc74uR89N2t6+1+lAp5n91CsdKvFuZuajGhJmfHMPMT475nMj6ZuxEHInr0+wx8v7Adc/4ffguEbmy2AIRCyCq0C1ai78O1kAnW6CKss5y6Ni3ra7llNKhrv/YUcDYUT6fF62S8NbO56CTjMA1H6BrdAzuvWmKbf81eb8gXqpHWG0FpPISXDznaiD2Up+/RU8Z3Qc/bbU36b+6/iCev3OS7Xmt0YLduTUY3z0KEZqWv0kbDSZ8/OonGFF+AqOffR6Ch9GRrWWb0Exsvp6RUVEAnBOEh5efwN9GxyJm5IXAoQwAwFU690nEgiji4ngT/udmRv602kKMq8/BugT7JJ/f7MjAPRf8wek4KeMglh+w1v9v351BhGTEimv6Qh3pPJDDUlMFVZT1C/zTP9i7dGsMZkSHef/z+yXGeUK3e2KL8VZlEnQWIwwNC2+eUHfBQK+v6F8MRIg6gMUhRwQNydqTRjv3y3rKESFyYpGQZKiwPnbTwjH3jxNdtrXG6LRoaGQLTA0L4R0NS8am4yWYOsC6kNySrVnYVWzGeQnAo5cNavF6P58owrqek6z/6mqAWP/Ow9HYIqJSNR+IpHfRuWxLVFkQc96FAIB7x3XDz2eqcONF53q8RmpqEnDctQull7EMV82+Auu+L7Rt25B2Lm6rqoYuxj6t+gfrdgA97F1ddaIWV689i88vcx7WbdbX2wKRnFr7PUFfVIzonqnN1rM5o4f3wZunMtClW1fc0JDDakru2fxJ7YjDV4g6wKly6yR+alny+M3UcaSMysuhhdQJWRwmdmznPv37BjiPmFm6uwSWhtEgu4qt5djhZY9Nyd7fbI+N9S2vGOwre4tI8x9rKlHAbYOc50zRSvYk1MsHdMEzF/dyWl6hqcHp9u6d+45ZZwIXZAn3zByHyDjnFARJUOGzNz9F6S87bNu+7OE+32b79zucnptM1vvAicMnESXZf2a1xUUey9aUsemQHgBGXThSJk9F2JDheP6Snlh8WS/0jfdiXHg7YYsIUQd4eb+1q6WimcWmxMJcIKIhX4QtIuRJcprtYXuvCTN1fH+Uy1lYftL+IVh3/CiiR5zjdJxksUBsJig6fjwbK1T20Sl1lVXQdfPvNObeds0AwMzR3dErpRZPb7EO103v51trQEpiDJ4bXY64cDXSbv4Hmk4HtnhiNNRV5Xhz51lkxKbj87QL8XkmcG/GGlx28x89XvclybllqbC4HJ/tysZXtbGAaG/Jqa3zPpCrcZP82t1hVtjBSRFeX6u9sEWEqJ21NKtqI9Fov7mog2c9KgoyQnQsxH+/AfGFDzrk9a6dkO70fMNZa+ueYyvCntVfQpbc/55LsoxHdtc5bdu48hsYMo97TLrMLa7E62t/xdls76dgb1xGQeXF2imCIGB0ahQWTOyKG3qqcPE034fHDx3cC2m909zu6983DennDMMQS5nT9jc1QzFzlX2xwn92ycXiSV09vsZDh0RrENJE6eEj+OKlt/DV9pbXyKmtdZ7PRC2ZIQbRgncAAxGidnfPmmNeHacy2QMRVZLnzH4iITkNQpxvw0L9ZUVFLGZ8nAGjaM9PedY8GHd/uAuVOdbk1mO/7sU7H/+A0gP7kbVnv8s1Pu5zOa7/RULZ77+77Dt0LBt/+j4fG2pjcPXKo1jxzFKYc8+4HFdRb8ZxhxEn5oaYxptApNGFfeNx0wX9223ht4tmXdLs/h49ktE/LR5Pjgr36bovRZ+L97tdhHfPCMg61fwcIXV1zqNyzi864NNrdQQGIkTtrNxhCfik+jKPx4kOTcot5NsRdaiHe7Y8S2ixLg7ffLgGv/+8B387GYGv0R13HArDQ8c9f8iu2FeI/bmVsFRVQjYZUV5Zh//b49x6sqr3xbhpUyXyTpzCzn0nbS2Mt645iUe+O4MzWdYPYrNs/aPRBNGnWnxi8wm56oYJ1M4Z0hPTYlo3E2vl2Zxm95sc8nH66wtw/92eu4YChTkiRO2sl1CLM7I1Oe7Vy3p4PC5s/PlAQ6utN/3cRB3lggtGYdhve3BbRvPrhK1MvwQrT3ve/5luF240jLc936zpic1bW+5+Mai0uG9Xw6rtR45j7nD76JJj239Fr/SrYWlsEQmiboeYMDWemdYDERoVju7+He+UOCeyRiVa6yEIAh64ahQuPnwKFUVleCEnDGbRu4/nZRm1GNXMXHMmg/Xn1ktfiMV3TWpVPdpbEMWOweX4oROY8XEGZnycEfQre1Jw0zaMgHlcexxhaZ4DkfB4+821pcx/oo4WN3qMy7bzSr1f3K2bvhTh187Fa8PMiG8cftwMQZZxmdr9YmzLDjq0LIrWJFkzrAGIOsiaE4d3i0Tf+DBMv3Q8vrh5EKY4tHxouzi3mAwe1hdX3/xHpyDklsxv8Ke87z1ePzMqFV+//hEsldafye6Thfh4+0nb/uoc61Tx4cY6t+cHA7aIePDejyeBWGuS1qkT2eg7oFeAS0RKZZYBCIBK2/ziYeEOwwUjVQx+Kbg4jtAZVp6Jf989FaLOOsojv9qIe7+0L96XWleMGyOKUS6pcahOA1FfgweungAA6D5yGJb0qMDcbws8vtYr54joPrg/unVLxoaXtzVbrp+MsbgEDoFIkAfx91w+AmVfH8boJG2Lo55ijDW45vEHYSkrRtzSV7A1YSR2dB2JlLpi/PPqc/CnDdZumXdiJ6B45Vb0NpZiSZcLAACrzmQ0XMU6Oqmb3nO3cKAxEPEgXrIn+EjFBQADEWqFX89WI0uwNseqdc0HIhGOgYiagQgFnzCLEfUqLVLCBYg6+7wTKdFafD57IOrLyhAREwVBax+GOtPNdWLj4/DhJTLe+nQzkJCEnbCOHFnYrQBDpk0CYA18NGoVroitw/rKCCwepcazv1WhXOU83PR3rfWD1tKQI9JOead+E6YW8fTM4V4daxFECGo11F1TMP7JpzA+4yD0X7wD7dz5UCc4d5OtixwCRHq4EIBEuXU5KB2BgYgbFknGji5DbM9z6yT0b+Z4Ik8+PWCfeEhd1fw3knCHabIjgqx5mQgAbuurxv7jObhh6jCXfSpRQGSi9yN5uiR1wd//fA0A4ERRDSxGIwZ1d52h9d6rxuCehu7xR3W5+Puv1S7HWGprFNMi4gvHLhpBowWGj0HEcHsX2cVJMjYWe3evmHHTFX4vn78wEHFjzaYDAOzRfmaJHpMCVhoKdkdPFeK1n7Iwt6eA8ZMnOO2LMtUCsLaEJPdIcXO2ndphQqhwTiRCQejy84fg8vOHtHygj/p3bT4JtlF0QhwA10DkwPJPYEYfQGMNiEKFWWh+5ty7pgzEoKxyvLLLfS4NACwZYkJ4YiJiU5u//wRS6ISOrWDMO4u6/y2DXFfrtD0j33l1yGofZrHzJ1mW8eL2PCzZkceE2SAiSxbI+3+FXGOdLfWrn4/irDoOz+bFwmI2Ox1rqbMmiN2SuxkJEzyvXQEAaof1yzXqwCzHTRTM0uLczwL6jSERp6K7AwitEWexJtegy1GYWsS0/glY3F8PtWS991yfsxWfDq7G3erTeKVfNdLPGd7il6BA69QtIn9//StsjxmDJz5ZhdF33Y68U2dRnpuP31TWyaTSq3ORFZ2GWjkw8VqtUcKPZ6wfduMj6nHeqOaXcab2V1lWgTeXfY+MmJ4Y9/1qnI5Kw7FYe/7Qxi+34rKrp9me10oiIAJ9h/SD0EKTseOkSiIDESIXTZM7L1EX43tzEvYk2ltpSk+dBiY3H/QHu3/tfxPL+1yBecfXAmh5EcP+48/BGtuoaGv31vTR7VY8v+vULSI/xQ6CLIjYUm+dQve+nbV4PNs+zntsgvXHo0fLHwqyyQRpx2bIFV6uAOUFx6nBFx02ojTzVDNHU3srLa3E3G8LsCNpBMp0cfgu7TynIAQA9FXO32BqGmL9yGYW0Grk2CLSOCSRiNyLkIyIiXRdSTfOXOvmaGUZPrAHFu19FX2TY1o+OAR02kCkzmhfCMhoNsNkce366J9sDVAOhndHwfcbYC4phrkgz7bfZJHxxq4CzPg4AzNXZWL51gyY/nany3Va261iNJqcnheezEJpaRXOns6FZDF7OIt8dabCgBX7i1Fnan7F2x37Ww4E6xpiR329EScOn0RtQyASlZTY4rkqh+CjvRczI1KquUOtH85/PjcF0dGuw0RGXz+zg0vkf+It8yHc9iDE+f8X6KJ0iE7bNbP/tTeApKkAgF+SRiDj8Emn/fGGSkSk9gKyKwAA9xT3RvyXp9DFWI3Fc2MhRkRiT04VNpyosJ3zea8p+LzXFKzOzYYmzbqa430f70UeIvDBpFjEp7XcTyfLsu1D6MfdGQDsfaJ7pTj8b4M1EBpWvg/P/OlyCPzm3GaPfJsFgwSUlVXgz1M8j4/KLasF0PyaEKt0gzH14GG88HsdTiIaEK2JqpFeBCIax3GHDESI3LpmVCouG9INkVoVNpaUAbB/0XumWxHELq4jb5RGCAuHeP7UQBejw3TKFhGDWcLzSc5v8j8OOn8bfmdGH3RPdf7wKNPFIjO6O/Sl1mGY1XnupyZe9+l6AMDm3zKR1xBIrP7m1xZbRnL37sfiV1bj93VfAwA+KXZOzPpfmT36P9SlH1Bd1ez1yDuGhlaMTfkWHNrjeabIgjrr78gNpuP47+QkAMC9RVux4pJEXKCtsB33674T1iDEQWRUy0ttO2b7C0xOJvKosaszMkzjtL137+RAFIfaqFMGIjq1iO4W9x/iN2Z9j3VjTVAnJKFLuBoLUmtcjqk0WLtFciqNbq/xVYJ1nPfyg+W2bd9EDsLMT47hg2XfeizXd0eLsT1hOP5Z2w//29v8iooAUFPqv3yUUFJjMMNgbr6bpVG92Xnp8v875tpImF1UiRkfZ2Cf2prEPLBHAnqnJmDdjf1x2QN3ITopEb0j7UGE2eA6ykoV3nIg4ihJ4135iTqzMJ3973VqwR5E9xsQwNJQa3XKQAQAls4+BysG10ItO9/w/zCiF4SB9lnvLpw81uXch7eXQ5IkfFHqfqbMSm00ZnycgTKN69j4dap0ZP+802W7JMv4AvZ1SFYcbX7YFgDM2WnGzox87DpytsVjO4taoxk3rz6JPy3bDanOfdJaYY0RC3/MwZGiOhRVuc42aNy3G4A1B+gfGzLxwEbnlq/0gdap/wWVCoLaeiO8auo5tt+lZREjnY6PMtUCLUzv3uil3S/hmX2vI0HFHCCilpgdBhLcfIXrvZqUodMGImqtFgOnX4W7E+0tI2smAN2vanmJ5FpNBP678mfb84T6Cnw2sxeiBO8+PH4+WQrJoen94/V7MOuTYz6U3u753yrx7L5a1FexmwYAMk9bZzIt1sZCn2XN+zFbJDz/6Q78+397IMsyNpyowC9na/DYxmw88G22yzWuOxINfUUF9p4pxcFS54Th+chAfPdUl3N0Oi0ujah02X53+U58NKjG6+TT3rUFGFJ5GrBILR5L1NmNHmDNu4u21CNhsPJzQzqrThuINLr4kon4e0IRPjxXC3W/QW4/MO4+vtZl21Ypyfb49T/2RXhkOBZN924i+IOmcNz88SF899NBAMCq8uZnFXw6MtP2eGDladyT7LqK4qnMXJj44QWNyd7CkVtt7SJZtel37JTisccYhU0/H0J+tfsuNUc3flOA53aWOG1LNZRhysxpHs4AonSu3TrnndMX6onNrNHtgRDWfFIsEVnn3vni5kFYMXdUoItCbdDpAxG1SsS5l12ILn08TxZ2+ZUX4L0d/8LntV+57IuSDAhLsgYl3WN0uC+h3Gn/uhv7Y9UNA7DuyiTMibbmdBwOT0OdoMHr2Rrc/9GvTsfPrjmAP4vHnbYNmfIHxMgGaCUTHrt2LC6bPMqlHI8dEfDEih1e1TmUGQz2IOOR77JQlJOPlcX26fpfOaPBzrOueT+J9eX4R9QZj9d9fbABb9xxHtSRnoPGqyaNQE9TOfrrC3BD1vf426GP0KVfP5/KL9x8L4SJk4FzJrR8MBFRCOi0w3d9oRr/ByQMGAzExuPJvcfwdIZ93yO6kwDsOQGXXXYuLpUkVBksiNKpIIgidAAQlwBtQiJQ7Twa4qw61vZ4nnACl999Hb746QiQYz9GExWFV68dCr1JQpdoz7kGR9QtDxENdXqDCYA1kz4vIgnlrzwPDL+3xfN61RZg3J2zgI8zXPb9WziAtNE3tHiN6MgwvHKbdUZHuXw4UFUBoZtrN05zxElXAJOCd3EqIiJ/6/QtIt4S4hIgCAIiIpxn8hs5Y7rrsaKI2HANVE2m9O7bq5vH6z8enokrb7oKoijiwuE9bdufjzkFQVQhNkyNZIcgZELxQbfXqT10wKv6uFNtsLQ4qVewswYido80E4Sk19gnp4syWbu7IlX2QLFPXQEm157EsOuu9rkcQpcECL36+nweEVFnwxYRH1nng7DmHlxduR+CzvsEqWHd4/CPi0QkamT85Qf7SIx/da/AyIuutD1PiI/B2tlREAQBguD++gvmTEbJd18h9YILYUntjWtWngAA3HRAh6fPbETf88ZDqKtBZGqqV4mSepOEOatPIFoNrLhBuUlfu85UAmrXobKJhkqU6GKdtp2bKCKrIaXkhkhrt9nzl/fBpsxKXD0kHrFhyv05EBEpBQMRH0VGRwCw5oFEJyb4fP647tbpif+UeBS5J8/gsinnIGWQaz6A2MICabrERKTdfDsA65vYD9W2SbSerOgBrLcGOk/GfI8RV14Cjar5YCQr1/pBXG0G6vV6hIUrM1lyj+i+e+qWiEK8bLEGIqPKjuH/7r4UMnrD9PonGGsuQOojjwEAesbqcPvorh1WXiKizo6BiI8cZ8hU17oO1/TWpZdOBC5teVVFb809Px1P/Fzisv3pql6I/3A3PrhjvJuz7OqKioCGMfmL1+3HH8elIzu/DFf8YTAEBO/aJ2dPncXZA4cx8bILUVBRB5NozQ+5rf4g9tRH4FCctXvkvJmX4OwXO1BRWo7758+yrYQ756E7AlZ2IiJiIOIznVqFcZUnsCemL0b26BLo4tiM7J2I1clRuHbNaZd9ZboY7P52C2bcebPH86uragBYWwx2S12w+9cKACIyP9iAvbo0XN5Vwo0Xj2qPordaaa0B9++sBdAbU15bg80p42z7Rvftij9dfx3yT52ErAuHoNHglhumBK6wRETkFgORVvj77PNRnXEEceN8nx+iPWnCwjDAXIrj6gRcaMnFj6o0274vM2swo8nxhjo9Pl27Hd3DJBgiY+HOZp11FtFPi4Ab/VROacdmoLwE4pXXu90vV5ZDiPUc5FkkGc9vPIldJfbEWscgBABSJoyHoFZDiI4FuG4LEVHQYiDSCuou8ehy7h8CXQy37r14CA7uOYQrJo3DgpgYPPPal9gdNwB6lfNon1qDCTetPQOgB1AP678WPPvtMYyLF3DJBN/Wc5BlGZt+3I9l2QIqxTAAqQBSsXDzTxgy5QKnY4u/+hyb95/BH3rFIG3unS7XqtMbMPvzrGZf7/GBgC7Ct7VdiIgoMBiIhJi+qV3Q94/2D/dLx/bF7pPAiZieqMgvtG2/aXWmu9Mx1XgamthYbNC7tkjsKpOxq0zGlqw9+MvUvoiOiYbeaIbGYsQv2w/goj8Mhyk8Gvk1RvRPsCa7miwS/rt0FX7qOsplsPhj+Uno+cHP+PPwSByrEXGsoBo/WoYAfYbgEwCfFhUhPCnJlp9y8sARLDjUfBLvEmEP0sfO8eInRUREwYCBSIiLibEPN16xZjMunX4Bbvrfcc/HqyRcf9V4pG7dj/5ddOgWqcYde52POWKJwrzvCwFYA5sYYw2qtEl47ZsCAAW2425IlbAyTwS6jvL4etnaBDxsW2bHeRTS7I1lAMqgkiywiCo0jWRmGzNw6UUj0aVPOkoKiiBVVKDrIAYhRERKwkAkxPUf2AvYaw08PtJ3w0dNgpAPB1ZizYEifBVmXSfnqnF9EKFRYcbFY2zHXJt7CKsLPf+qVGndT3u+Ms+19eL9cWocKKzDB6fMqHIz3wcAiLIFkmBfVdMahDS5ztR4JCTPtD1PTO4KJHPYLRGR0jAQCXGiKOJiy1lsVPVw2bfqD1roek3A3IE1GL/lJwwa0APaQcNcjpszdSgqPtmKH5DSqjKcW/Q75lw5Bt0HWIfSThkATLkAqK43ou5YBr4/cBY/G2JxdWQZps2aCpMmHHM+OwKj4Prr+Xj9Loy7+TqIXBSOiCgkCLKsjCEFxcXFMJlMLR/oJUEQkJKSgvz8fCjkR9BqOSfPYP6veqdtfxaOYepNTcfRNK/eYMLc1cdxXbIFY7vpsPbn4yiJSMBRIRbP6jKQ+IcLYC4qQMmRDPxTGgEAeGGQCQPGDPe5zJLFjN/2HMX3e7MxrnsUhvVKRPKgfhC1upZPRud6fxt1tjqzvqGts9UXCL06azQaJCUltXgcW0Q6ge79euGt8ELcs9U6I+xd8ZWYerlvQQgAhOk0WHXzUNvzh4YNBGAdFSMIDdtTuiJ15Ah80cYyiyo1xk0YjnETfA9iiIhIORiIdBIp3ZPx4wNDse1gJoamDPTrtYN11lUiIgp+DEQ6kfAwLUZ2jwuJJj8iIgoNzU/KQERERNSOGIgQERFRwDAQISIiooBhIEJEREQBw0CEiIiIAoaBCBEREQUMAxEiIiIKGAYiREREFDAMRIiIiChgGIgQERFRwDAQISIiooBRzFozanX7FLW9rhusWN/Q19nqzPqGts5WXyB06uxtPQSZK6ARERFRgHTarhm9Xo9HH30Uer0+0EXpEKxv6OtsdWZ9Q1tnqy/QOesMdOJARJZlZGVlobM0CLG+oa+z1Zn1DW2drb5A56wz0IkDESIiIgo8BiJEREQUMJ02ENFoNLj22muh0WgCXZQOwfqGvs5WZ9Y3tHW2+gKds84AR80QERFRAHXaFhEiIiIKPAYiREREFDAMRIiIiChgGIgQERFRwDAQIcWqqqqCJEmBLgYRkVd4z3Iv5AKRkpISrF27FocOHUJtbS0AhPwsdcXFxXj11VexatUq7N27FwBC/pd99+7deOutt5CXlxfoorQ7s9mMTz/9FHv27Al0UTpESUkJ3n77bXz55ZfYv38/gND/fe5s9y3es8hRSAUiOTk5eOyxx5CdnY2vvvoKH330EXJyciAIQsj+UR85cgQLFiyAwWDAmTNnsGTJEpw8eRKiKIZknRvrFB4ejj179uDQoUOor68PcKnaV1lZGXbs2IE+ffrYtoXiewsAe/fuxUMPPYSamhpkZ2fjtddeQ1ZWVsj+PgOd777Fe1bo37N8FVKByPHjx5GamooHH3wQDzzwAHr06IE33ngDACAIQoBL1z52796NKVOmYMGCBZg/fz7OPfdcrFmzBkBo1rmxTmVlZQCA7OxsZGZmBrJI7UqWZSQkJCAsLMxWZyA031sA2L9/P2666SY89NBDuPPOOzF8+HDs3LkTQOjWubPdt3bt2sV7Vgjfs1pD0YGI2WwGYI84S0pKbM2aUVFRuOqqq1BfX49vv/02YGX0t6Z1tlgsiIuLAwCEhYUhLS0NGo0GRqMxUEX0q8b6Nmqsd3R0NIYOHQpBEHDs2DGUl5c77VeqpvUVBAHFxcXQ6/UoLi7G119/jRdeeAFbtmxBTU0NAGXXuenvM2D9Owas3yCrqqowbNgw2z4l17VRbm4uiouLYTAYAACFhYUhfd9qWl+j0RjS96zG+jbWp/F3PFTvWf6g2EDkp59+wptvvum0rW/fvpBl2SnavO2227B+/XpUVFR0cAn9z7HOjc2248aNwx/+8AfIsgxRFKHValFUVAStVhvg0radu/e48dtFZmYmevfujUsvvRSnT59GVlaW034lcldfwHqzNplM+Pnnn1FXV4cePXrgyy+/xPr162EwGBRb56a/zwAwcuRI7N+/H5988gmeffZZHD58GOvXr8c///lPlJWVKbaugHWJ9+effx6PPfYYXnrpJbzyyisAgEGDBkGSpJC7bzWt72uvvQYAOP/883H++eeH3D3L0/urVqsBhOY9y18UF4g0Ro9nz57FTz/9hIyMDNsbmZSUhB49emDfvn2244cPH460tDSsWrUKgDITotzVGbD+Ag8fPhyJiYm2n4HRaERycjIAa2uJEnmqL2B//xISElBRUYGePXsiPT0dR48excqVK23N+ErSXH0BoK6uDjU1NRgyZAiuv/563HjjjZg2bRqysrJw9uzZQBS5TZqr75gxY3DrrbciJycHAPDBBx9g3rx5iI6OxjvvvAOTyRSQMvvDtm3bYLFYsGTJEsyYMQPHjh3Dxo0b0bdvX3Tv3j3k7ltN63v06FFs2LABQ4cORVJSUkjdswDX+h4/fhzffPONbX8o3bP8TXGBSOMvb01NDXr37o0PPvjA9kfas2dP9O3bF/n5+Thy5IjtnOuvvx67du1CTU0NRFFxVXZb56bNeY0/g4qKCts+lUrVsQX1k+bq2/j+nTx5Ej169ABgreeGDRuwfft2JCUlBabQbdDS+5uQkICYmBin93PSpEnIyspCXV1dh5e3rVqq7/Dhw2E2mzF8+HCEhYUhPj4ed911F/bv3++UJ6M0O3fuxMCBAxEfH4+xY8fi4osvxqFDhxATE4MBAwaE3H2raX0vueQSHDlyxHavCqV7FuBa32nTpuHYsWO2eobSPcvfgv63+8yZM1i3bh2OHz9uu+nm5+ejqqoK//jHP1BcXIyNGzfajh83bhyioqKwceNG27cnjUaDlJQUxdy0vanz999/73RO440qKysL55xzDgDg999/x7vvvhv036Z8qW/jN6bU1FRkZmbiySefxIYNGzBmzBgMGjRIETdsX99fk8mEMWPG4MCBA7b6q1QqhIeHKyL73tf6Go1GpKamOuUMHDlyBFFRUbZcimDnWOfGMk+YMAGDBg0CYG2ur6+vt/1tjh49WtH3LW/qq9frIQgCRFGEJEkhc8/ypr4AkJaWpth7VnsL6p/A4cOH8cQTTyA/Px9r1qzBsmXLUFlZiZSUFNxxxx2Ijo7GLbfcgpUrV9oS95KTk3HRRRfBYDDgueeew/Hjx7F+/XqUlJQgMjIywDVqWWvqDNi/XWi1WtTU1ODll1/GwoULER4eHtS/6L7Wt/EbU0FBAfbt24f+/fvjpZdewn333QeNRhPUdQVa9/5GRUVh/PjxkCQJzz77LH777Te8/PLLEAQB/fr1C3CNmtea+mq1WiQnJ+Po0aNYunQptmzZglWrViE1NRWpqakBrlHLmtZ5+fLlqKurw5QpUzBs2DDbt//4+Hjbh1hqaqpi71u+1LfxPW4MRgDl37O8qS9gDb6VeM/qCIIcxCm7q1atQnZ2Nh5++GFUV1djzZo1KC4uxiOPPAJZlm1NvAsWLMDQoUNxxx132M7V6/V49913kZGRgdjYWNx4440YMWJEoKritbbU2WAwYO7cuQCAiRMn4p577kFERERA6uGt1ta3qqoKNTU1Th9Mjt+yglVb3t+SkhKsXLkSer0eERERuP322xEeHh6oqniltfXV6/XYsWMHvv/+exiNRvTr1w933nknwsLCAlkdr7irc0lJCR5++GEA9t/TV199FWFhYbjrrrts5yrxvtWa+jZuC5V7ljfvb3V1NaqrqxV3z+oI6kAXwNGePXvw448/YsiQIZg4cSLq6+ttQ76io6Nx66234r777sPu3bsxbtw4mM1mqNVq3HffffjHP/6BSy65BN27d4fFYkF4eDjmz5+Puro6REVFBe0QKX/VWZZlGI1GzJ49G+eccw569eoV4Jq556/6xsTEICYmxunawfgH7a/6SpKExMREzJ8/H0ajMWhHGPirvuHh4Zg6dSomTJgAi8WC2NjYANfMM1/r7Khr1662x3l5eUhNTcWf/vQn1NXVITo6OijvW/6oryiKKCgoQGxsbMjdsxw5vr+1tbVITU11CsCD8Z4VCEHzU1i1ahVeeeUVREREYNu2bXjqqaeQnp6OmpoaFBUVAbAmuV1//fVYvXq17QYmSRL69euH8ePH49133wVgT3gSRRFRUVG2c4ONP+ssCAKio6Mxc+bMoP2D9md9lcCf9XW8YQVrENIe729UVFRQByG+1tlkMtney5ycHPTp0wf5+fl48MEH8dJLL8FoNEKlUiE6Otp2bjDxV33//Oc/48UXX4RKpQqpe1Zz7++LL74Io9EYdO9pMAiKQKS2thYHDx7E3/72N9x777148MEHodFocODAAfTq1Qu//fab7dgpU6ZAkiTbsKjG5L158+bh5MmTKCgoCEgdfNXZ6sz6sr6NQqG+QOvqvH79egBAUVERysvL8d577+Evf/kLxo4di8WLFwdtkAn4t77jxo3DCy+80GnqO3bs2KCvbyAFRSDSmLCj0+kAWL8FpaamolevXoiPj0dWVhbOnDljO37WrFnYtGkTZFmGRqOB2WxGVFQU3n//fdt49GDX2erM+rK+oVRfoG11joyMhCiK6NGjB95++23ccsstgaqG11jf0K5vIAVFICKKIq6++mr06NEDkiQhKioKZWVlUKvVmDJlCkRRdJruODIyEklJSbaM88aZ65QUbXa2OrO+rG8o1RdoXZ0TExOh1+shiiKeffZZPPTQQ0Hd9eSI9Q3t+gZSUAQiarUaI0eOhE6ngyAIsFgsCAsLQ1RUFBITE3HhhRciJycHS5YswfHjx/Htt99Cr9crIoPek85WZ9aX9Q2l+gKtr7NWq0V4eDji4+MDXQWfsL6hXd9ACopAxJEgCFCpVCguLrYlnQ4ZMgR//vOfAQAvvPACjEYj5s2bZ/sWpXSdrc6sL+sbSvUFvK/zPffcExJ1Zn1Du74dLSh/YiUlJaivr8eoUaMAAKtXr0bXrl3xl7/8BTU1NYiMjAy5zOPOVmfWl/UNpfoCna/OrG9o17cjBWUgYjQa0a1bN+zcuRPr169HWVkZ/vrXvwKAbThuqOlsdWZ9Wd9Q09nqzPqGdn07UlAGIjk5OcjIyMDp06cxc+ZMzJo1K9BFanedrc6sL+sbajpbnVnf0K5vRwrKKd6zs7Oxb98+XH755YrKom+LzlZn1je0dbb6Ap2vzqwv+UtQBiJERETUOQTdqBkiIiLqPBiIEBERUcAwECEiIqKAYSBCREREAcNAhIiIiAKGgQgREREFDAMRIvK766+/HocPH2636xcVFeH6669HUVFRu70GEXUMBiJEnUBRURFWrVoV6GIE3K5du7Br165AF4OIHDAQIeoEiouLsXr16kAXI+B2796N3bt3B7oYROSAgQgREREFTFAuekdE/rFq1SqnlpDrr78eAHDRRRdh/vz5AID58+fjuuuuQ0pKCj777DOUl5djyZIltnMKCgrwwQcfICMjA1qtFmPHjsXtt99uW2+jvr4e77//Pn755ReEh4fj5ptvdinHjh07sGbNGhQUFCAtLQ233HILhg8f7nU9Kisr8dZbb+HAgQOIj4/HjBkzXI75+uuvsWHDBlRUVCAlJQW33HILRowYYatjcXGx7dht27YBAJ588kkMHToUAGAwGPDxxx9jx44dsFgsGDFiBO68807ExMR4XU4i8h0DEaIQNm3aNIwZMwanTp3CO++8g4ULFwIAoqOjnY47fvw4li9fjkmTJqFHjx627bIs4/nnn4dOp8PDDz+M+vp6vPvuu0hISMC1114LAFi2bBl27dqFO+64AxEREfjoo4+crn3o0CH897//xTXXXINhw4bh559/xnPPPYfFixcjLS3Nq3q8+uqryMnJwQMPPACj0ejyGtu3b8eyZcswZ84cDBgwAD///DNeeuklvPHGGwgPD8ejjz4Kk8mE//3vfwCA6667DgCQmppqu8Y777yDw4cP46677oJOp8Py5cuxePFi/Otf//KqjETUOgxEiEJYfHw84uPjUV9fDwDo27ev2+O2bt2Kp556CgMGDHDabjAYcMUVV2DYsGFITU2FJEnYtm0bTpw4AcDaGrJlyxbMnj0bkyZNAgCIooj//Oc/tmusXr0aY8aMsbXGDBo0CL/88gt+/vln27bm5Obm4sCBA/jrX/+KiRMnAgBqamrw4YcfOtXzgQcewAUXXAAACAsLw3fffYfc3Fz069cPPXv2BGAPwJr+HIqKivDTTz9hwYIFGD9+PADAYrHgP//5D4qKitC1a9cWy0lErcNAhIgwefJklyAEsH6gT5gwAdu2bcPhw4dx8uRJ1NTUYPDgwQCAwsJCWCwW9OvXz3ZO475G2dnZqKmpcQk68vPzvSpb43H9+/e3bRsyZIjTMUOGDMH+/fvxzjvv4Pjx48jJyQFgDaS8kZ2dDVmWsXjxYrevz0CEqP0wECEipw95RyUlJXj00UeRmpqKc889F7NmzcL+/fuRkZEBwNp1A1hbQRo5Pm50ySWXYNq0aU7bIiIivCqbJEktvsaKFSvw3Xff4eKLL8asWbMwaNAg3HvvvV5d39Hjjz+OuLg4p20MQojaF0fNEHUCGo0GAGA0Gn06b9euXairq8MTTzyBK664AoMGDUJBQYFtf9euXSEIAk6dOmXbduzYMadr9OjRAxUVFejdu7ft365du7B3716vypCcnAwAyMzMtG1rDIQabdq0CdOnT8fcuXNx3nnnQa/Xu72WRqNx+zNozIsxm822MsbGxuLLL79ESUmJV+UkotZhiwhRJ9C9e3eEh4fjiy++wNChQ5GdnY2JEye6fPtvKjo6GhaLBVu2bEFKSgq2bNmCHTt2YODAgQCsrRoXXHAB1qxZg6ioKLfJqtdeey2eeeYZfPrppxg5ciSOHTuGNWvW4K9//atXZe/ZsyeGDBmCDz/8ELIsw2g0YuXKlS7lPHjwIIYPH468vDxbUqrFYnE6rn///lixYgX2798PwNriM23aNHTr1g0XXngh3nvvPej1enTp0gXr1q3D2bNncffdd3tVTiJqHUFubFslopD222+/YdmyZSgqKkJiYiL+9a9/oUuXLrbhu43Jpo4kScKHH36I7du3Q6VSYejQoUhJScG3336L119/HREREaivr8eHH36I3bt3QxRFXH/99XjnnXechsY6Dt/t2rUrZsyY4fb1PKmsrMS7776L33//HZGRkZg+fTo+/PBDvPrqq+jatSuOHTuG999/H3l5eUhOTsZVV12Fjz76CJdeeqlTbkpjfX766ScYjUZccMEFti4cx+G7RqMRgwYNwty5c9G9e/c2/dyJqHkMRIgoYGRZtuWAuCMIgtucEyIKHeyaIaKA2bZtG15//XWP+3v06IEXX3yxA0tERB2NLSJEFDA1NTXNJoNqtVqnSceIKPQwECEiIqKAYecrERERBQwDESIiIgoYBiJEREQUMAxEiIiIKGAYiBAREVHAMBAhIiKigGEgQkRERAHDQISIiIgC5v8BJ26oWX+UUZUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          17.24  17.28\n",
       "1          17.15  17.29\n",
       "2          17.67  17.68\n",
       "3          17.86  17.96\n",
       "4          17.92  18.05\n",
       "...          ...    ...\n",
       "3600       10.96  11.14\n",
       "3601       11.23  11.39\n",
       "3602       11.20  11.50\n",
       "3603       11.19  11.29\n",
       "3604       11.39  11.43\n",
       "\n",
       "[3605 rows x 2 columns]>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11.19],\n",
       "       [11.21],\n",
       "       [11.32],\n",
       "       ...,\n",
       "       [17.06],\n",
       "       [17.23],\n",
       "       [16.92]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>11.39</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.11</td>\n",
       "      <td>11.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>11.19</td>\n",
       "      <td>11.29</td>\n",
       "      <td>10.79</td>\n",
       "      <td>11.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>11.20</td>\n",
       "      <td>11.50</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>11.23</td>\n",
       "      <td>11.39</td>\n",
       "      <td>10.83</td>\n",
       "      <td>11.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>10.96</td>\n",
       "      <td>11.14</td>\n",
       "      <td>10.92</td>\n",
       "      <td>11.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>17.92</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.61</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>17.86</td>\n",
       "      <td>17.96</td>\n",
       "      <td>17.60</td>\n",
       "      <td>17.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>17.67</td>\n",
       "      <td>17.68</td>\n",
       "      <td>16.99</td>\n",
       "      <td>17.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>17.15</td>\n",
       "      <td>17.29</td>\n",
       "      <td>16.70</td>\n",
       "      <td>17.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>17.24</td>\n",
       "      <td>17.28</td>\n",
       "      <td>16.75</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3605 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  11.39  11.43  11.11  11.19\n",
       "14614.0  11.19  11.29  10.79  11.21\n",
       "14615.0  11.20  11.50  11.10  11.32\n",
       "14616.0  11.23  11.39  10.83  11.05\n",
       "14617.0  10.96  11.14  10.92  11.14\n",
       "...        ...    ...    ...    ...\n",
       "20076.0  17.92  18.05  17.61  17.87\n",
       "20077.0  17.86  17.96  17.60  17.67\n",
       "20080.0  17.67  17.68  16.99  17.06\n",
       "20081.0  17.15  17.29  16.70  17.23\n",
       "20082.0  17.24  17.28  16.75  16.92\n",
       "\n",
       "[3605 rows x 4 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.11</td>\n",
       "      <td>11.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.29</td>\n",
       "      <td>10.79</td>\n",
       "      <td>11.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>11.20</td>\n",
       "      <td>11.50</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>11.23</td>\n",
       "      <td>11.39</td>\n",
       "      <td>10.83</td>\n",
       "      <td>11.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>10.96</td>\n",
       "      <td>11.14</td>\n",
       "      <td>10.92</td>\n",
       "      <td>11.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3600</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>17.92</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.61</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3601</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>17.86</td>\n",
       "      <td>17.96</td>\n",
       "      <td>17.60</td>\n",
       "      <td>17.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3602</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>17.67</td>\n",
       "      <td>17.68</td>\n",
       "      <td>16.99</td>\n",
       "      <td>17.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>17.15</td>\n",
       "      <td>17.29</td>\n",
       "      <td>16.70</td>\n",
       "      <td>17.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>17.24</td>\n",
       "      <td>17.28</td>\n",
       "      <td>16.75</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3605 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  11.39  11.43  11.11  11.19\n",
       "1     14614.0  11.19  11.29  10.79  11.21\n",
       "2     14615.0  11.20  11.50  11.10  11.32\n",
       "3     14616.0  11.23  11.39  10.83  11.05\n",
       "4     14617.0  10.96  11.14  10.92  11.14\n",
       "...       ...    ...    ...    ...    ...\n",
       "3600  20076.0  17.92  18.05  17.61  17.87\n",
       "3601  20077.0  17.86  17.96  17.60  17.67\n",
       "3602  20080.0  17.67  17.68  16.99  17.06\n",
       "3603  20081.0  17.15  17.29  16.70  17.23\n",
       "3604  20082.0  17.24  17.28  16.75  16.92\n",
       "\n",
       "[3605 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x287c534c890>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\76496\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - loss: 0.0837 - val_loss: 0.0101\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0124 - val_loss: 0.0023\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0013 - val_loss: 1.2423e-04\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.1694e-04 - val_loss: 1.0520e-04\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.9961e-04 - val_loss: 1.0590e-04\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.9412e-04 - val_loss: 9.8065e-05\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.8095e-04 - val_loss: 8.3217e-05\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.6260e-04 - val_loss: 6.6213e-05\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.4340e-04 - val_loss: 5.8955e-05\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.2986e-04 - val_loss: 6.1961e-05\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.2036e-04 - val_loss: 6.7482e-05\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.1304e-04 - val_loss: 7.3528e-05\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.0714e-04 - val_loss: 8.0035e-05\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.0098e-04 - val_loss: 8.5649e-05\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.9456e-04 - val_loss: 9.0127e-05\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.8874e-04 - val_loss: 9.4721e-05\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.8227e-04 - val_loss: 9.8985e-05\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.7595e-04 - val_loss: 1.0272e-04\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.6904e-04 - val_loss: 1.0465e-04\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.6282e-04 - val_loss: 1.0626e-04\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.5703e-04 - val_loss: 1.0592e-04\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.5079e-04 - val_loss: 1.0518e-04\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.4400e-04 - val_loss: 1.0324e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.3695e-04 - val_loss: 1.0036e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.2893e-04 - val_loss: 9.5223e-05\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.2029e-04 - val_loss: 9.0598e-05\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.1124e-04 - val_loss: 8.4955e-05\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.0111e-04 - val_loss: 7.9822e-05\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.9012e-04 - val_loss: 7.6082e-05\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.7917e-04 - val_loss: 7.3590e-05\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 9 (\t) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.69056\n",
      "均方根误差: 0.83100\n",
      "平均绝对误差: 0.62107\n",
      "R2: 0.75787\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
